Child voiceprint multi-sound-source analysis method and system

Abstract

The invention discloses a child voiceprint multi-sound-source analysis method and system. The method comprises the following steps: acquiring an original voice signal; identifying a voiced segment and a multi-peak energy structure of the original voice signal so as to judge whether a plurality of sound sources are overlapped or not; when a plurality of sound sources are overlapped, analyzing a current frame frequency spectrum of the original voice signal, and comparing the current frame frequency spectrum with a pre-established child voice feature mask, marking and suppressing non-target interference frequency components to identify a child sound source so as to obtain a time-frequency mask track; and performing weighted suppression on the frequency spectrum in the original voice signal by using the time-frequency mask track, eliminating noise by using an adaptive Wiener filter, and reconstructing a target child voice signal. By implementing the method provided by the invention, the sound source of the target child can be effectively distinguished and enhanced, the voiceprint stability of the child is ensured, and the voiceprint matching precision is improved.

Description

Technical Field

[0001] This invention relates to voiceprint analysis methods, and more specifically to a method and system for multi-source voiceprint analysis of children. Background Technology

[0002] In a home environment, multiple sound sources significantly impact the accuracy of children's voiceprint recognition. Specifically, the sounds of parents conversing in different rooms create complex reflections and reverberation through walls and door cracks, increasing the complexity of background noise. Televisions, tablets, and stereos continuously output background speech, which highly overlaps with children's voices in frequency bands, further interfering with the clarity of their voices. The loud noise generated by children playing, such as slapping, dragging, and collisions, significantly reduces the speech signal-to-noise ratio, making it even more difficult to capture effective speech signals.

[0003] Traditional voiceprint systems based on single-channel feature extraction and template matching typically assume the target speaker is dominant, making it difficult to handle issues of overlapping and alternating sound sources. Furthermore, while conventional noise reduction methods can improve speech intelligibility to some extent, in the diverse noise types and rapidly changing statistical characteristics of a home environment, key details of children's speech can be weakened, especially under multi-source aliasing conditions. The formant structures of other speakers may contaminate the target child's voiceprint features, increasing the matching distance and reducing recognition accuracy. Children's vocal cords are still developing, and pitch and formant positions vary significantly with age, height, and emotional state, resulting in considerable fluctuations in children's voiceprints. Simultaneously, children often mix crying, laughing, singing, and screaming into complex vocal patterns, making traditional feature extraction methods based on stable speech segments unsuitable. Without adaptive correction, the system can easily misidentify different speakers. Most current voiceprint recognition systems are primarily designed for adults, ignoring the characteristics of children, such as a higher proportion of high-frequency energy and shorter vocal tract length, leading to unstable performance in children's application scenarios. Existing systems lack an overall architecture for multi-source aliasing, and are particularly inadequate in separating multiple sound sources to enhance the sound quality for children.

[0004] Therefore, it is necessary to design a new method to effectively distinguish and enhance the target child's voice source, while ensuring the stability of the child's voiceprint and improving the accuracy of voiceprint matching. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for multi-source voiceprint analysis of children.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for multi-source voiceprint analysis of children, comprising:

[0007] Acquire the raw speech signal;

[0008] The original speech signal is identified by identifying vocal segments and multi-peak energy structure to determine whether multiple sound sources overlap.

[0009] When multiple sound sources overlap, the current frame spectrum of the original speech signal is analyzed, and non-target interference frequency components are marked and suppressed by comparing it with a pre-established child speech feature mask to identify the child sound source and obtain the time-frequency mask trajectory.

[0010] The frequency spectrum in the original speech signal is weighted and suppressed using the time-frequency mask trajectory, and noise is eliminated using an adaptive Wiener filter to reconstruct the target child's speech signal.

[0011] The further technical solution is as follows: the identification of vocal segments and multi-peak energy structure in the original speech signal to determine whether multiple sound sources overlap includes:

[0012] The original speech signal is preprocessed, framed, and its short-time energy and zero-crossing rate are calculated to identify sound segments and multi-peak energy structures in order to determine whether multiple sound sources overlap.

[0013] The further technical solution is as follows: Preprocessing the original speech signal, framing, calculating short-time energy and zero-crossing rate, identifying audible segments and multi-peak energy structures to determine whether multiple sound sources overlap, includes:

[0014] The original speech signal is processed using a pre-emphasis filter to enhance the high-frequency components and balance the spectrum to obtain a pre-processed signal.

[0015] The preprocessed signal is divided into several sampling points to form multiple time window signals, so as to obtain the signal of each frame;

[0016] The short-time energy of each frame of signal is calculated to identify sound segments. By detecting the extreme values ​​of the energy envelope and combining them with a set energy threshold, when the short-time energy of several consecutive frames exceeds the energy threshold, it is confirmed that the signal has a multi-peak energy structure.

[0017] Calculate the average zero-crossing rate of each frame of signal to distinguish different types of phonemes and noise types;

[0018] By combining short-time energy and average zero-crossing rate, when high energy is found to be accompanied by an abnormally high zero-crossing rate, it can be determined that there is multi-source aliasing or strong noise interference in the current environment.

[0019] The further technical solution is as follows: when multiple sound sources overlap, the current frame spectrum of the original speech signal is analyzed, and by comparing it with a pre-established child speech feature mask, non-target interference frequency components are marked and suppressed to identify the child's sound source, thereby obtaining the time-frequency mask trajectory, including:

[0020] Based on the data from the registration phase, the key frequency bands of the target children's speech were determined to obtain the children's characteristics;

[0021] Perform a Fast Fourier Transform on the original speech signal to obtain the spectrum, and calculate the cosine similarity between the spectrum and the mask of the child's features;

[0022] When the cosine similarity is lower than a preset threshold, the spectrum is marked as non-target interference, and a binarized frequency mask is generated.

[0023] The binarized frequency mask is smoothed by applying medium-frequency filtering to remove isolated noise points and form a continuous time-frequency mask trajectory.

[0024] The further technical solution is as follows: The method of using the time-frequency mask trajectory to perform weighted suppression on the spectrum of the original speech signal, using an adaptive Wiener filter to eliminate noise, and reconstructing the target child's speech signal includes:

[0025] The time-frequency mask trajectory is used to perform a dot product operation on the spectrum of the original speech signal to suppress non-children's voice source components in the frequency domain, so as to obtain the remaining signal components;

[0026] The remaining signal components are then enhanced a second time to obtain the enhanced spectrum;

[0027] The enhanced spectrum is subjected to an inverse fast Fourier transform, and the time-domain continuous waveform is recovered using the overlap-addition method to obtain the target child's speech signal.

[0028] A further technical solution is as follows: the secondary enhancement of the remaining signal components to obtain the enhanced spectrum includes:

[0029] The remaining signal components are then enhanced a second time using a Wiener filter based on prior signal-to-noise ratio estimation to obtain the enhanced spectrum.

[0030] The further technical solution is as follows: the remaining signal components are enhanced a second time using a Wiener filter based on prior signal-to-noise ratio estimation to obtain the enhanced spectrum, including:

[0031] The remaining signal components are subjected to a Wiener filter based on prior signal-to-noise ratio estimation. The gain function of the Wiener filter is dynamically adjusted according to the local signal-to-noise ratio. While preserving the formant structure of the child's speech, residual stationary background noise is filtered out to obtain the enhanced spectrum.

[0032] This invention also provides a multi-source voiceprint analysis system for children, comprising:

[0033] The acquisition unit is used to acquire the raw speech signal;

[0034] The judgment unit is used to identify the sound segments and multi-peak energy structure of the original speech signal in order to determine whether there are multiple overlapping sound sources.

[0035] The trajectory determination unit is used to analyze the current frame spectrum of the original speech signal when multiple sound sources overlap, and to mark and suppress non-target interference frequency components by comparing with a pre-established child speech feature mask to identify the child sound source and obtain the time-frequency mask trajectory.

[0036] The suppression unit is used to perform weighted suppression of the spectrum in the original speech signal using the time-frequency mask trajectory, eliminate noise using an adaptive Wiener filter, and reconstruct the target child's speech signal.

[0037] The further technical solution is as follows: the judgment unit is used to preprocess, frame, calculate short-time energy and zero-crossing rate of the original speech signal, identify the sound segment and multi-peak energy structure, so as to determine whether there are multiple sound sources overlapping.

[0038] The further technical solution is as follows: the judgment unit includes:

[0039] The preprocessing subunit is used to process the original speech signal using a pre-emphasis filter to enhance the high-frequency components and balance the spectrum to obtain a preprocessed signal.

[0040] The segmentation subunit is used to segment the preprocessed signal into several sampling points to form multiple time window signals, so as to obtain the signal of each frame;

[0041] The short-time energy calculation subunit is used to calculate the short-time energy of each frame of signal, to identify sound segments, and to confirm the existence of a multi-peak energy structure in the signal when the short-time energy of several consecutive frames exceeds the energy threshold by detecting the extreme value of the energy envelope and combining it with a set energy threshold.

[0042] The average zero-crossing rate calculation subunit is used to calculate the average zero-crossing rate of the signal for each frame in order to distinguish different types of phonemes and noise types;

[0043] The determination subunit is used to combine short-time energy and average zero-crossing rate. When high energy is found to be accompanied by an abnormally high zero-crossing rate, it is determined that there is multi-source aliasing or strong noise interference in the current environment.

[0044] The advantages of this invention compared to existing technologies are as follows: This invention acquires the original speech signal and identifies the vocal segments and multi-peak energy structure to determine whether multiple sound sources overlap. Once multiple sound sources overlap is confirmed, the current frame spectrum is analyzed and compared with a pre-established child speech feature mask. Non-target interference frequency components are marked and suppressed, thereby accurately identifying and separating the child's voice source and generating a time-frequency mask trajectory. Then, the original speech signal is weighted and suppressed using this mask, and an adaptive Wiener filter is used to further eliminate residual noise, ultimately reconstructing a clear target child's speech signal. This method not only effectively distinguishes and enhances the target child's voice source, but also ensures the stability of the child's voiceprint by optimizing high-frequency component processing, dynamically adjusting the template and threshold, etc., while improving the matching accuracy based on multi-dimensional voiceprint vectors including MFCC, F0, and other features, ensuring high-accuracy child voiceprint recognition even in complex acoustic environments.

[0045] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the multi-source voiceprint analysis method for children provided in an embodiment of the present invention.

[0048] Figure 2 This is a schematic block diagram of a multi-source voiceprint analysis system for children provided in an embodiment of the present invention;

[0049] Figure 3 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0052] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0053] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0054] Please see Figure 1 , Figure 1 This is a flowchart illustrating the multi-source voiceprint analysis method for children provided in this invention. This method is applied in a server. Through a series of steps, it effectively distinguishes and enhances the target child's voice source while ensuring the stability of the child's voiceprint and improving voiceprint matching accuracy. First, after acquiring the original speech signal, pre-emphasis, frame segmentation, and calculation of short-time energy and zero-crossing rate are used to identify vocal segments and multi-peak energy structures, determining whether multiple voice sources overlap. Next, for detected multi-source cases, the current frame spectrum is analyzed using Fast Fourier Transform and compared with a pre-established child speech feature mask. Non-target interference frequency components are marked and suppressed, generating a time-frequency mask trajectory for identifying the child's voice source. Then, the original speech signal is weighted and suppressed using this time-frequency mask trajectory, combined with an adaptive Wiener filter to eliminate noise and reconstruct the target child's speech signal. Finally, secondary enhancement based on prior signal-to-noise ratio estimation is performed on the remaining signal components, dynamically adjusting to retain key features of the child's speech and filter out background noise, thereby improving the accuracy of voiceprint matching. This process precisely separates the target child's voice from the frequency domain perspective, ensuring the stability and clarity of the voiceprint in complex environments.

[0055] Figure 1 This is a flowchart illustrating the multi-source voiceprint analysis method for children provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps S110 to S140.

[0056] S110, Acquire the original speech signal.

[0057] In this embodiment, the raw speech signal refers to unprocessed sound data directly captured from the environment using a microphone or other audio acquisition device. This signal may contain information from multiple sound sources, such as a child's voice, a parent's voice, television, or background music. To ensure coverage of the frequency bands of the child's voice and its environmental noise, the sampling frequency is typically set to 16000Hz. This step is fundamental to all subsequent processing, and its quality directly affects the final recognition and separation results.

[0058] S120. Identify the sound segments and multi-peak energy structure of the original speech signal to determine whether there are multiple overlapping sound sources.

[0059] In this embodiment, the original speech signal is preprocessed, framed, and its short-time energy and zero-crossing rate are calculated to identify sound segments and multi-peak energy structures in order to determine whether multiple sound sources overlap.

[0060] In one embodiment, step S120 described above may include steps S121 to S125.

[0061] S121. The original speech signal is processed using a pre-emphasis filter to enhance the high-frequency components and balance the spectrum to obtain a pre-processed signal.

[0062] In this embodiment, a pre-emphasis filter is first used to process the original speech signal, and its transfer function is H(z) = 1 - 0.97z. -1 The aim is to enhance the high-frequency components of the signal, thereby balancing the entire spectrum. This is because high-frequency components are relatively important in children's voices, while natural speech signals tend to decay faster in the high-frequency range.

[0063] S122. Divide the preprocessed signal into several sampling points to form multiple time window signals, so as to obtain the signal of each frame.

[0064] Next, the signal is divided into multiple time windows (frames) using an overlapping segmentation method. Each frame has a length of 512 sampling points (corresponding to 32ms) and a frame shift of 256 sampling points (corresponding to 16ms). A Hamming window is chosen as the window function, with the expression w(n) = 0.54 - 0.46cos(2πn / (N-1)), where 0 ≤ n ≤ N-1, to ensure short-time stationarity.

[0065] S123. Calculate the short-time energy of each frame of signal to identify the sound segment, and by detecting the extreme value of the energy envelope and combining it with the set energy threshold, when the short-time energy of several consecutive frames exceeds the energy threshold, it is confirmed that the signal has a multi-peak energy structure.

[0066] In this embodiment, short-time energy is defined as the sum of the energy of each frame of signal, and the calculation formula is as follows: The system sets an energy threshold Eth. This threshold is set when the energy of K consecutive frames (e.g., K=5) is low for a short period. When the value is greater than Eth, it is considered that the sound segment has been entered. Furthermore, within the sound segment, extreme value detection is performed on the energy envelope. If multiple energy peaks are detected within a unit time window (e.g., 500ms) and the depth of the trough between these peaks is less than 30% of the energy of the main peak, then a multi-peak energy structure is considered to exist, suggesting that multiple sound sources may overlap.

[0067] S124. Calculate the average zero-crossing rate of each frame of signal to distinguish different types of phonemes and noise types.

[0068] In this embodiment, the average zero-crossing rate refers to the number of times the signal crosses the zero level, and the calculation formula is as follows: , where sgn[.] represents the sign function. This index helps distinguish between unvoiced, voiced, and noise types.

[0069] S125. Combining short-time energy and average zero-crossing rate, when high energy is found to be accompanied by an abnormally high zero-crossing rate, it is determined that there is multi-source aliasing or strong noise interference in the current environment.

[0070] Finally, by combining the two indicators of short-time energy and average zero-crossing rate, if high energy is detected along with an abnormally high zero-crossing rate (exceeding the statistical range of single-person speech), it can be preliminarily determined that there is multi-source aliasing or strong background noise interference in the current environment. This analysis provides a basis for further sound source separation.

[0071] S130. When multiple sound sources overlap, analyze the current frame spectrum of the original speech signal, and by comparing it with a pre-established child speech feature mask, mark and suppress non-target interference frequency components to identify the child sound source and obtain the time-frequency mask trajectory.

[0072] In this embodiment, the time-frequency mask trajectory refers to a binary matrix on the time-frequency plane used to distinguish the target child's voice source from other interfering voice sources. Each element in this matrix corresponds to a signal component within a specific time point and frequency range, where "1" indicates preservation (i.e., considered to belong to the target child's voice source) and "0" indicates suppression (i.e., considered not to belong to the target child's voice source). Through this step, a pure child's voice signal can be extracted from a complex multi-source environment, providing higher quality input for subsequent voiceprint recognition.

[0073] In one embodiment, step S130 described above may include steps S131 to S134.

[0074] S131. Based on the data from the registration phase, determine the key frequency bands of the target child's speech to obtain the child's characteristics.

[0075] In this embodiment, child characteristics refer to the key frequency bands and spectral patterns obtained from the target child's speech data and used to identify their unique vocal features.

[0076] First, during the registration phase, speech data of the target children is collected, and their long-term average power spectral density (PSD) in the 0-8000Hz range is calculated to identify key frequency bands with concentrated energy. Typically, the fundamental frequency F0 of children's speech fluctuates between 250Hz and 600Hz, while the first and second formants are located relatively high. This information is used to construct a feature mask, serving as the basis for subsequent steps to determine whether a voice source belongs to the target child.

[0077] S132. Perform a Fast Fourier Transform on the original speech signal to obtain the spectrum, and calculate the cosine similarity between the spectrum and the mask of the child's features.

[0078] In this embodiment, the original speech signal is then subjected to a Fast Fourier Transform (FFT) to convert it to the frequency domain, obtaining the spectrum Xi(k) of the current frame. This spectrum is then compared with the previously established target child speech feature mask by calculating the cosine similarity between the two. Cosine similarity is an index that measures the consistency of the directions of two vectors, with a value ranging from -1 to 1; a larger value indicates a higher similarity.

[0079] S133. When the cosine similarity is lower than a preset threshold, the spectrum is marked as non-target interference, and a binarized frequency mask is generated.

[0080] In this embodiment, the binarized frequency mask refers to a two-dimensional matrix that marks whether a sound source belongs to the target child by comparing the current frame spectrum with the child's features, where 1 represents retention (belonging to the target child sound source) and 0 represents suppression (not belonging to the target child sound source).

[0081] If the calculated cosine similarity is lower than a preset threshold gamma (e.g., 0.6), the spectrum of the current frame is considered to not match the target child's speech, and is therefore marked as "non-target interference." The generated binarized frequency mask M(i,k) is a two-dimensional matrix, where 1 represents preservation (i.e., considered to belong to the target child's voice source) and 0 represents suppression (i.e., considered not to belong to the target child's voice source). This process effectively achieves preliminary filtering of non-child voice source components in the mixed speech signal.

[0082] S134. Apply medium-frequency filtering to smooth the binarized frequency mask, remove isolated noise points, and form a continuous time-frequency mask trajectory.

[0083] Finally, to further improve the mask quality, median filtering was used to smooth the generated binarized frequency mask. Median filtering is a commonly used image processing technique that can effectively remove isolated noise points while maintaining sharp edges. After this step, the time-frequency mask trajectory is more continuous and smoother, which helps to extract the target child's voice signal more accurately from complex backgrounds.

[0084] Through the above steps, the system can accurately separate the target child's voice signal in a multi-sound source environment, providing a reliable foundation for subsequent voiceprint recognition.

[0085] S140. The spectrum in the original speech signal is weighted and suppressed using the time-frequency mask trajectory, and noise is eliminated using an adaptive Wiener filter to reconstruct the target child's speech signal.

[0086] In this embodiment, the target child's voice signal refers to the sound signal that is separated and enhanced from the mixed voice signal after processing, and that is specifically belonging to the target child. This process ensures that even if multiple sound sources (such as parents, television, etc.) exist in the same space, the voice features of the target child can be clearly extracted for subsequent voiceprint recognition or other interactive applications.

[0087] In one embodiment, step S140 described above may include steps S141 to S143.

[0088] S141. The frequency spectrum in the original speech signal is multiplied using the time-frequency mask trajectory to suppress non-children's voice source components in the frequency domain, so as to obtain the remaining signal components.

[0089] First, the spectrum Y(i,k) of the original speech signal is multiplied by the previously constructed binarized time-frequency mask M(i,k), i.e., S_hat(i,k)=Y(i,k)M(i,k). This step aims to directly suppress frequency components that do not belong to the target child's voice source in the frequency domain, thereby preserving those components that match the voice characteristics of the target child.

[0090] S142. Perform secondary enhancement on the remaining signal components to obtain the enhanced spectrum.

[0091] In this embodiment, the remaining signal components are enhanced a second time using a Wiener filter based on prior signal-to-noise ratio estimation to obtain the enhanced spectrum.

[0092] Specifically, a Wiener filter based on prior signal-to-noise ratio estimation is applied to the remaining signal components. The gain function of the Wiener filter is dynamically adjusted according to the local signal-to-noise ratio. While preserving the formant structure of the child's speech, residual stationary background noise is filtered out to obtain the enhanced spectrum.

[0093] Next, for the remaining signal components S_hat(i,k) obtained in the above steps, a secondary enhancement is performed using an adaptive Wiener filter based on prior signal-to-noise ratio estimation. The Wiener filter dynamically adjusts its gain function G(k) according to the local signal-to-noise ratio to effectively filter out residual stationary background noise, while preserving the key formant structure of the child's speech as much as possible, thus obtaining a clearer spectrum of the target child's speech.

[0094] S143. Perform an inverse fast Fourier transform on the enhanced spectrum and use the overlap-addition method to recover the time-domain continuous waveform to obtain the target child's speech signal.

[0095] Finally, an inverse fast Fourier transform (IFFT) is performed on the enhanced spectrum S_hat(i,k) to convert it back to the time domain signal. To ensure the continuous and uninterrupted time-domain waveform, an overlap-addition method is used to smoothly connect the signals of each frame, ultimately forming a high-quality target child's speech signal. This process not only improves the intelligibility and naturalness of the target child's speech but also provides more accurate and reliable input data for subsequent voiceprint recognition.

[0096] In this embodiment, the technical fields involved in the method include multi-source speech separation technology and child voiceprint recognition technology. Firstly, regarding multi-source speech separation technology, this technology aims to model and distinguish multiple speech signals from parents, children, television, music, etc., within the same space. It employs source separation and enhancement algorithms based on the power spectrum distribution, spatial location, and temporal structure characteristics of different sound sources, and achieves real-time processing and online adaptation of aliased speech on a low-computing-power embedded platform. Secondly, the child voiceprint recognition technology focuses on building a robust acoustic feature extraction model within the range of children's voice frequencies, considering the high-frequency energy concentration and rapid frequency fluctuations of children's voices. This technology also includes matching strategy design based on multi-dimensional voiceprint vectors (such as distance measurement, threshold setting, and adaptive updates), and dynamic correction of the stability of children's voiceprints to ensure that the system maintains reliable recognition even under conditions of changes in children's pitch, emotional fluctuations, and environmental changes.

[0097] In terms of application scenarios, the method in this embodiment can be used in children's smart toys, supporting personalized addressing and dialogue in multi-sound source scenarios such as family living rooms and bedrooms. It prioritizes the identification and locking of the target child's voice source to avoid confusion in dialogue, and executes differentiated interaction strategies based on voiceprint results. In addition, it is also suitable for multi-terminal collaborative scenarios in the home, realizing "toy-first response" for children through voiceprint recognition, supporting linkage with parents' mobile apps to record voice interaction logs or learning progress, and performing role differentiation and permission control when multiple devices pick up voices simultaneously.

[0098] The core issues to be addressed include separating multiple sound sources to enhance the quality of children's voice sources, ensuring the stability of children's voiceprints, and improving voiceprint matching accuracy. Specific measures include determining the presence of multiple speakers under single-microphone or limited-microphone conditions; distinguishing the target child's voice source from other interfering sound sources when multiple sound sources are present and enhancing it accordingly; and improving the signal-to-noise ratio of children's speech in the target frequency band. Simultaneously, the system automatically models the natural fluctuation range of children's voiceprints, using an adaptive template update mechanism based on historical records to ensure correct recognition even as children's voices change slowly with age, and employing specific strategies in extreme tone cases. Furthermore, by constructing a multi-dimensional voiceprint vector including MFCC, F0, formant positions, and other robust time-frequency features, a distance metric and decision threshold suitable for children are designed, and the template and threshold are dynamically adjusted using an adaptive correction mechanism.

[0099] The multi-source analysis module determines the number of sound sources based on energy and temporal structure analysis. First, the original speech signal x(n) is acquired via a microphone, with a sampling frequency of fs=16000Hz to cover children's speech and most environmental noise frequencies. Pre-emphasis is applied with an alpha coefficient of 0.97. The signal is then framed using an overlapping segmentation method, with a Hamming window as the window function. The frame length N is set to 512 sampling points (corresponding to 32ms), and the frame shift is set to 256 sampling points (corresponding to 16ms). Next, the short-time energy Ei of each frame is calculated. When the energy of K consecutive frames exceeds the threshold Eth, it is considered a sound segment. Extreme value detection is performed on the energy envelope to identify potential multi-peak energy structures indicating multi-source overlap. Furthermore, the short-time average zero-crossing rate ZCR (Zero Crossing Rate) is calculated to distinguish between unvoiced, voiced, and noise levels. An abnormally high zero-crossing rate is preliminarily identified as multi-source aliasing or a noisy environment. Based on spectral differences, the target child's voice source is identified. A feature mask is established to statistically analyze the key frequency bands of the target child's speech. The similarity between the current frame's spectrum and the child's feature mask is calculated. Non-target interference is labeled to generate a binarized time-frequency mask M(i,k), which is then smoothed using median filtering to form a continuous time-frequency mask trajectory. Finally, the target signal is enhanced using filtering and masking methods. The generated time-frequency mask is used to perform a dot product operation on the mixed speech spectrum to suppress non-child voice source components. An adaptive Wiener filter is used for secondary enhancement, and an IFFT is performed on the enhanced spectrum to recover the target child's speech signal.

[0100] The method in this embodiment significantly improves the recognizability of children's voiceprints in multi-source aliasing scenarios. Through multi-source analysis and target child voice source enhancement, it maintains a high recognition success rate even in the presence of television, parental conversations, and environmental noise. It is specifically optimized to address the rich high-frequency components of children's voices, reducing the energy encroachment of interfering sound sources on key frequency bands. In the sound source separation stage, the mixed speech is input and undergoes frame-level preprocessing, including pre-emphasis, framing, and windowing. Short-time energy, ZCR, and simplified spectrum are calculated for each frame. A multi-source detection algorithm is used to determine whether multiple speakers or strong background speech are present. If multiple sound sources are detected, the multi-source analysis module is invoked to enhance the target sound source based on the child's feature mask, outputting the child's enhanced speech result. If a single sound source is detected, the speech is directly sent to the next stage.

[0101] The aforementioned method for multi-source analysis of children's voiceprints acquires the original speech signal and identifies the vocal segments and multi-peak energy structure to determine if multiple voice sources overlap. Once overlapping is confirmed, the current frame spectrum is analyzed and compared with a pre-established children's speech feature mask. Non-target interference frequency components are marked and suppressed, thereby accurately identifying and separating the children's voice sources and generating a time-frequency mask trajectory. This mask is then used to apply weighted suppression to the original speech signal, and an adaptive Wiener filter is employed to further eliminate residual noise, ultimately reconstructing a clear target children's speech signal. This method not only effectively distinguishes and enhances the target children's voice sources but also ensures the stability of children's voiceprints through optimized high-frequency component processing, dynamic template and threshold adjustments, etc. It also improves the matching accuracy based on multi-dimensional voiceprint vectors, including features such as MFCC and F0, ensuring high-accuracy children's voiceprint recognition even in complex acoustic environments.

[0102] Figure 2 This is a schematic block diagram of a multi-source voiceprint analysis system 300 for children provided in an embodiment of the present invention. Figure 2 As shown, corresponding to the above-described method for multi-source voiceprint analysis of children, the present invention also provides a multi-source voiceprint analysis system 300 for children. This multi-source voiceprint analysis system 300 includes a unit for executing the above-described method for multi-source voiceprint analysis of children, and the system can be configured in a server. Specifically, please refer to... Figure 2 The child voiceprint multi-source analysis system 300 includes an acquisition unit 301, a judgment unit 302, a trajectory determination unit 303, and a suppression unit 304.

[0103] The acquisition unit 301 is used to acquire the original speech signal; the judgment unit 302 is used to identify the vocal segments and multi-peak energy structure of the original speech signal to determine whether multiple sound sources overlap; the trajectory determination unit 303 is used to analyze the current frame spectrum of the original speech signal when multiple sound sources overlap, and to mark and suppress non-target interference frequency components by comparing with a pre-established child speech feature mask to identify the child sound source and obtain the time-frequency mask trajectory; the suppression unit 304 is used to use the time-frequency mask trajectory to perform weighted suppression on the spectrum in the original speech signal, use an adaptive Wiener filter to eliminate noise, and reconstruct the target child speech signal.

[0104] In one embodiment, the judgment unit 302 is used to preprocess, frame, calculate short-time energy and zero-crossing rate of the original speech signal, identify sound segments and multi-peak energy structure, so as to determine whether there are multiple overlapping sound sources.

[0105] In one embodiment, the determination unit 302 includes:

[0106] The preprocessing subunit processes the original speech signal using a pre-emphasis filter to enhance the high-frequency components and balance the spectrum to obtain a preprocessed signal. The segmentation subunit divides the preprocessed signal into several sampling points, forming multiple time window signals to obtain each frame signal. The short-time energy calculation subunit calculates the short-time energy of each frame signal to identify sound segments and, by detecting the extreme values ​​of the energy envelope and combining them with a set energy threshold, confirms the presence of a multi-peak energy structure when the short-time energy of several consecutive frames exceeds the energy threshold. The average zero-crossing rate calculation subunit calculates the average zero-crossing rate of each frame signal to distinguish different types of phonemes and noise types. The determination subunit combines short-time energy and average zero-crossing rate; when high energy is found accompanied by an abnormally high zero-crossing rate, it determines that there is multi-source aliasing or strong noise interference in the current environment.

[0107] In one embodiment, the trajectory determination unit 303 includes:

[0108] The key frequency band determination subunit is used to determine the key frequency bands of the target child's speech based on the data from the registration stage, so as to obtain the child's features; the calculation subunit is used to perform a fast Fourier transform on the original speech signal to obtain the spectrum and calculate the cosine similarity between the spectrum and the mask of the child's features; the frequency mask generation subunit is used to mark the spectrum as non-target interference when the cosine similarity is lower than a preset threshold and generate a binarized frequency mask; the removal subunit is used to apply medium-level filtering to smooth the binarized frequency mask, remove isolated noise points, and form a continuous time-frequency mask trajectory.

[0109] In one embodiment, the suppression unit 304 includes:

[0110] The dot product subunit is used to perform a dot product operation on the spectrum of the original speech signal using the time-frequency mask trajectory to suppress non-child voice source components in the frequency domain to obtain the remaining signal components; the secondary enhancement subunit is used to perform secondary enhancement on the remaining signal components to obtain the enhanced spectrum; the transform subunit is used to perform an inverse fast Fourier transform on the enhanced spectrum and use the overlap-add method to recover the time-domain continuous waveform to obtain the target child speech signal.

[0111] In one embodiment, the secondary enhancement subunit is used to perform secondary enhancement on the remaining signal components using a Wiener filter based on prior signal-to-noise ratio estimation to obtain the enhanced spectrum.

[0112] In one embodiment, the secondary enhancement subunit is used to apply a Wiener filter based on prior signal-to-noise ratio estimation to the remaining signal components. The gain function of the Wiener filter is dynamically adjusted according to the local signal-to-noise ratio to filter out residual stationary background noise while preserving the formant structure of the child's speech, so as to obtain the enhanced spectrum.

[0113] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned children's voiceprint multi-source analysis system 300 and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0114] The aforementioned multi-source voiceprint analysis system 300 for children can be implemented as a computer program, which can perform functions such as... Figure 3 It runs on the computer device shown.

[0115] Please see Figure 3 , Figure 3 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a server, wherein the server can be a standalone server or a server cluster composed of multiple servers.

[0116] See Figure 3 The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0117] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a method for analyzing a child's voiceprint from multiple sources.

[0118] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0119] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a method for analyzing the multi-source voiceprint of a child.

[0120] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0121] The processor 502 is used to run a computer program 5032 stored in a memory to implement all the steps of the child voiceprint multi-source analysis method.

[0122] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0123] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0124] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein when executed by a processor, the computer program causes the processor to perform all the steps of the child voiceprint multi-source analysis method.

[0125] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0126] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0127] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of each unit is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0128] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the system of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0129] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0130] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for multi-source voiceprint analysis of children, characterized in that, include: Acquire the raw speech signal; The original speech signal is identified by identifying vocal segments and multi-peak energy structure to determine whether multiple sound sources overlap. When multiple sound sources overlap, the current frame spectrum of the original speech signal is analyzed, and non-target interference frequency components are marked and suppressed by comparing it with a pre-established child speech feature mask to identify the child sound source and obtain the time-frequency mask trajectory. The frequency spectrum in the original speech signal is weighted and suppressed using the time-frequency mask trajectory, and noise is eliminated using an adaptive Wiener filter to reconstruct the target child's speech signal.

2. The method for multi-source voiceprint analysis of children according to claim 1, characterized in that, The step of identifying vocal segments and multi-peak energy structures in the original speech signal to determine whether multiple sound sources overlap includes: The original speech signal is preprocessed, framed, and its short-time energy and zero-crossing rate are calculated to identify sound segments and multi-peak energy structures in order to determine whether multiple sound sources overlap.

3. The method for multi-source voiceprint analysis of children according to claim 2, characterized in that, The preprocessing, framing, short-time energy and zero-crossing rate calculation of the original speech signal, and the identification of vocal segments and multi-peak energy structures to determine whether multiple sound sources overlap, include: The original speech signal is processed using a pre-emphasis filter to enhance the high-frequency components and balance the spectrum to obtain a pre-processed signal. The preprocessed signal is divided into several sampling points to form multiple time window signals, so as to obtain the signal of each frame; The short-time energy of each frame of signal is calculated to identify sound segments. By detecting the extreme values ​​of the energy envelope and combining them with a set energy threshold, when the short-time energy of several consecutive frames exceeds the energy threshold, it is confirmed that the signal has a multi-peak energy structure. Calculate the average zero-crossing rate of each frame of signal to distinguish different types of phonemes and noise types; By combining short-time energy and average zero-crossing rate, when high energy is found to be accompanied by an abnormally high zero-crossing rate, it can be determined that there is multi-source aliasing or strong noise interference in the current environment.

4. The method for multi-source voiceprint analysis of children according to claim 1, characterized in that, When multiple sound sources overlap, the current frame spectrum of the original speech signal is analyzed, and non-target interference frequency components are marked and suppressed by comparing it with a pre-established child speech feature mask to identify the child sound source and obtain the time-frequency mask trajectory, including: Based on the data from the registration phase, the key frequency bands of the target children's speech were determined to obtain the children's characteristics; Perform a Fast Fourier Transform on the original speech signal to obtain the spectrum, and calculate the cosine similarity between the spectrum and the mask of the child's features; When the cosine similarity is lower than a preset threshold, the spectrum is marked as non-target interference, and a binarized frequency mask is generated. The binarized frequency mask is smoothed by applying medium-frequency filtering to remove isolated noise points and form a continuous time-frequency mask trajectory.

5. The method for multi-source voiceprint analysis of children according to claim 1, characterized in that, The process of using the time-frequency mask trajectory to perform weighted suppression on the spectrum of the original speech signal, using an adaptive Wiener filter to eliminate noise, and reconstructing the target child's speech signal includes: The time-frequency mask trajectory is used to perform a dot product operation on the spectrum of the original speech signal to suppress non-children's voice source components in the frequency domain, so as to obtain the remaining signal components; The remaining signal components are then enhanced a second time to obtain the enhanced spectrum; The enhanced spectrum is subjected to an inverse fast Fourier transform, and the time-domain continuous waveform is recovered using the overlap-addition method to obtain the target child's speech signal.

6. The method for multi-source voiceprint analysis of children according to claim 5, characterized in that, The secondary enhancement of the remaining signal components to obtain the enhanced spectrum includes: The remaining signal components are then enhanced a second time using a Wiener filter based on prior signal-to-noise ratio estimation to obtain the enhanced spectrum.

7. The method for multi-source voiceprint analysis of children according to claim 6, characterized in that, The step of performing secondary enhancement on the remaining signal components using a Wiener filter based on prior signal-to-noise ratio estimation to obtain the enhanced spectrum includes: The remaining signal components are subjected to a Wiener filter based on prior signal-to-noise ratio estimation. The gain function of the Wiener filter is dynamically adjusted according to the local signal-to-noise ratio. While preserving the formant structure of the child's speech, residual stationary background noise is filtered out to obtain the enhanced spectrum.

8. A multi-source voiceprint analysis system for children, characterized in that, include: The acquisition unit is used to acquire the raw speech signal; The judgment unit is used to identify the sound segments and multi-peak energy structure of the original speech signal in order to determine whether there are multiple overlapping sound sources. The trajectory determination unit is used to analyze the current frame spectrum of the original speech signal when multiple sound sources overlap, and to mark and suppress non-target interference frequency components by comparing with a pre-established child speech feature mask to identify the child sound source and obtain the time-frequency mask trajectory. The suppression unit is used to perform weighted suppression of the spectrum in the original speech signal using the time-frequency mask trajectory, eliminate noise using an adaptive Wiener filter, and reconstruct the target child's speech signal.

9. The children's voiceprint multi-source analysis system according to claim 8, characterized in that, The judgment unit is used to preprocess, frame, calculate short-time energy and zero-crossing rate of the original speech signal, identify sound segments and multi-peak energy structure, so as to determine whether there are multiple overlapping sound sources.

10. The children's voiceprint multi-source analysis system according to claim 9, characterized in that, The determination unit includes: The preprocessing subunit is used to process the original speech signal using a pre-emphasis filter to enhance the high-frequency components and balance the spectrum to obtain a preprocessed signal. The segmentation subunit is used to segment the preprocessed signal into several sampling points to form multiple time window signals, so as to obtain the signal of each frame; The short-time energy calculation subunit is used to calculate the short-time energy of each frame of signal, to identify sound segments, and to confirm the existence of a multi-peak energy structure in the signal when the short-time energy of several consecutive frames exceeds the energy threshold by detecting the extreme value of the energy envelope and combining it with a set energy threshold. The average zero-crossing rate calculation subunit is used to calculate the average zero-crossing rate of the signal for each frame in order to distinguish different types of phonemes and noise types; The determination subunit is used to combine short-time energy and average zero-crossing rate. When high energy is found to be accompanied by an abnormally high zero-crossing rate, it is determined that there is multi-source aliasing or strong noise interference in the current environment.

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