13 results about "Normal speech" patented technology

The present application belongs to the technical field of intelligent teaching, and particularly relates to a teaching quality evaluation method and system based on deep learning. The method comprises extracting feature data to be evaluated from normal speech, evaluating the feature data to be evaluated by using a speech evaluation model to generate an evaluation result, and determining the quality grade of the normal speech according to the evaluation result, wherein the feature extraction from noise speech and noise speech comprises extracting amplitude information and frequency information from a sound production section. The present application performs screening on teaching speech to identify abnormal sound sections; through voiceprint comparison, the teaching speech in which the abnormal sound sections that can match the pre-stored voiceprint are classified as noise speech, and the teaching speech that cannot be matched is classified as noise speech, so as to distinguish the noise speech originating from the background environment from the noise speech originating from the teaching subject, overcome the evaluation error problem caused by regarding the two as noise without distinction, and lay a data foundation for subsequent evaluation.

This invention relates to the field of speech recognition technology, specifically to a method for suppressing false triggering in a speech recognition system. This method acquires and segments candidate wake-up speech signals, performs audible acoustic analysis and high-frequency carrier anomaly analysis on each speech segment to be verified, and calculates a command validity score by combining cross-segment consistency, audible injection risk, and target secondary discrimination threshold. Based on this, false triggering speech signals are suppressed. This invention combines candidate recognition, segment verification, audible acoustic analysis, and high-frequency carrier anomaly analysis, and comprehensively considers cross-segment physical consistency, audible injection risk, and target secondary discrimination threshold to generate a command validity score, thereby improving the accuracy, security, and reliability of false triggering recognition and normal speech command response.

The invention relates to the technical field of speech recognition, and discloses a phoneme recognition model training method, a phoneme recognition method and a phoneme recognition device.A first sample phoneme sequence corresponding to normal speech data is recognized through a first phoneme recognition model, and a second sample phoneme sequence corresponding to dysarthria speech data is recognized through a second phoneme recognition model; and taking the second sample phoneme sequence as a reference phoneme sequence in a scene without a fixed text. According to the method, fine tuning training is performed on the basis of the first phoneme recognition model according to the individual dysarthria data of the dysarthria patient to obtain the individualized second phoneme recognition model, and the situation of free dialogue or poor text compliance can be covered even in a scene without a fixed text, so that the accuracy of recognition of the dysarthria is improved. The actual pronunciation ability of the patient can be reflected in a natural communication context, dependence on fixed texts and artificial phoneme level alignment labeling is avoided, higher flexibility and adaptability are achieved, and a reliable basis can be provided for rehabilitation training and curative effect tracking.

Embodiments of the present application disclose a speech processing method and an electronic device, and relate to the technical field of information processing. The method comprises: obtaining original speech; preprocessing the original speech to obtain a plurality of original speech features; in the case where the plurality of original speech features comprise a first speech feature, extracting a voiceprint feature from the first speech feature to obtain a first voiceprint feature, the speech type corresponding to the first speech feature being a whisper speech type; determining a target voiceprint feature corresponding to the first voiceprint feature from a target voiceprint feature library; and performing whisper speech conversion on the first speech feature based on the target voiceprint feature to obtain converted normal speech. According to the present application, the same tone as the real tone of the user or the tone specified by the user can be restored in the whisper speech conversion process, thereby improving the user experience.

The invention relates to the technical field of voice information security, and discloses a recording interference prevention method and device, and the method comprises an environment perception step, an equipment recognition and positioning step, an interference strategy decision-making step, a dynamic sequence generation step, a signal fusion construction step, a parametric array modulation step, a directional emission step, and an evaluation adjustment step. The device corresponds to the method. According to the application, different recording devices are accurately distinguished through an acoustic fingerprint identification technology, and a targeted interference strategy can be generated according to specific acoustic features of the devices; by combining an acoustic parametric array and a beam forming technology, accurate directional transmission of interference sound waves is realized, interference energy is enabled to act on target equipment in a concentrated manner, and the interference efficiency is improved; through fusion of multiple dynamic sequences and construction of composite baseband signals, the unpredictability and anti-filtering capability of interference signals are enhanced. Therefore, the high-efficiency interference performance is maintained, the influence on normal voice communication is reduced, and accurate and self-adaptive anti-recording protection is realized.

A computer may train a single-class machine learning using normal speech recordings. The machine learning model or any other model may estimate the normal range of parameters of a physical speech production model based on the normal speech recordings. For example, the computer may use a source-filter model of speech production, where voiced speech is represented by a pulse train and unvoiced speech by a random noise and a combination of the pulse train and the random noise is passed through an auto-regressive filter that emulates the human vocal tract. The computer leverages the fact that intentional modification of human voice introduces errors to source-filter model or any other physical model of speech production. The computer may identify anomalies in the physical model to generate a voice modification score for an audio signal. The voice modification score may indicate a degree of abnormality of human voice in the audio signal.

The application discloses an anti-adversarial audio defense method and system based on audio conversion and transcription difference detection, and belongs to the technical field of artificial intelligence security. The method comprises the following steps: step 1, standardizing and multi-dimensionally converting an input original audio; step 2, calculating a character error rate between the original audio and an audio sample transcription result, and comparing the character error rate with a pre-set detection threshold; and step 3, triggering a security response mechanism when the audio contains potential adversarial interference, and submitting the audio to an automatic speech recognition model for transcription processing when the audio is normal speech. The application can realize rapid detection and interception of adversarial audio samples without modifying an existing automatic speech recognition model, significantly improves the stability and robustness of detection, enables the system to realize real-time detection with low delay, balances high accuracy and low false alarm rate, and meets the actual needs of a voice interaction scene.

The embodiment of the invention provides a vehicle-mounted voice protection method, device and equipment and a computer readable storage medium. The method comprises the following steps: acquiring an in-vehicle voice signal in real time; analyzing the voice signal to obtain power spectrum distribution characteristics; according to the power spectrum distribution characteristics, multiple paths of periodic short pulse sequences are constructed; generating a multi-channel ultrasonic masking signal according to the multi-path periodic short pulse sequence; and transmitting the multi-channel ultrasonic masking signal by using an ultrasonic phased array, and forming an ultrasonic sound field in a target area in the vehicle. In this way, the directional interference characteristic of the ultrasonic phased array can be utilized, accurate noise injection of illegal recording equipment is achieved, and therefore vehicle-mounted voice privacy is effectively protected under the condition that normal voice communication is not affected.

This invention provides a noise reduction and sound pickup device and method. The noise reduction and sound pickup device includes at least one noise pickup, a target sound pickup device, and a sound signal processing unit. The at least one noise pickup is positioned close to a corresponding noise source, suitable for acquiring ambient noise emitted by the noise source and generating an ambient noise frequency signal. The target sound pickup device is positioned close to a target sound source, suitable for acquiring the target sound from the target sound source and generating a target sound frequency signal. The sound signal processing unit inverts and amplifies at least a portion of the frequency signal in the ambient noise frequency signal, superimposes the inverted and amplified ambient noise signal with the target sound signal to obtain a useful sound, and outputs the useful sound. The noise reduction and sound pickup device and method can acquire and eliminate various complex noises emitted by large equipment, while simultaneously preserving and amplifying the required sound signal to ensure normal voice communication and improve communication quality.

This application relates to a method for automatically switching between mesh calls and 5G data network calls. Specifically a Bluetooth device constructs a mesh network to form a mesh group One of the nodes is used as the master node, and an online loop is created on the AP P. All Bluetooth devices communicate with the application in a two-way dynamic heartbeat data mode, and the cloud synchronizes information in the virtual network formed by mapping the nodes; The voice data between online nodes is communicated in mesh mode; when the nodes of Bluetooth devices are offline or reconnected, the node device automatically switches to the data network mode through the application and performs network communication using the data network mode via the cloud. This automatic switching method between mesh calls and 5G data network calls monitors the online status of the device in real time and automatically switches the voice data transmission to the cloud network when the device / node goes offline, thereby ensuring the normal voice data transmission and reception of non - communicating nodes and ensuring the real - time nature, continuity and completeness of voice data transmission. ​

Provided is an information processing device that perform processing related to speech conversion of a speech that is not normally uttered and does not include pitch information such as a whisper or a faint speech. The information processing device includes a speech-to-unit encoder that generates an acoustic unit from a speech waveform, and a unit-to-speech decoder that reconstructs a speech waveform from an acoustic unit. The unit-to-speech decoder is subjected to preliminary learning by self-supervised learning of a Masked Language Model type using a normal speech and a whisper without a text label of a specific speaker to generate an acoustic unit common to the normal speech and the whisper, the acoustic unit being a latent expression in which a difference between the normal speech and the whisper is absorbed.

The invention discloses a pseudo-ear language generation method, device and equipment. The pseudo-ear language generation method comprises the following steps: acquiring normal voice; extracting multi-scale acoustic features of the normal voice; based on the multi-scale acoustic features, determining sounding effort capable of representing sounding force and clearness; and performing joint modulation of the cross-domain acoustic parameters based on the sounding effort, wherein the joint modulation comprises at least two of a time domain, a frequency domain and an excitation domain. The acoustic parameters of the time domain are adjusted based on the sounding effort, so that dynamic fluctuation caused by semantic key points, emotion emphasis or breathing rhythm in real ear language can be reflected; the acoustic parameters of the frequency domain are adjusted based on the sounding effort, so that the phenomenon that the sounding is clearer by force can be simulated; the acoustic parameters of the excitation domain are adjusted based on the sounding effort, and the effect of enhancing the high-frequency hoarseness sound during forced blowing can be simulated; therefore, the naturalness and interpretability of the pseudo-ear language can be effectively improved.

The application discloses an ultrasonic transducer driving circuit and an electronic artificial larynx, and relates to the field of medical devices. In the ultrasonic transducer driving circuit, a power module is used for providing power supply for the whole driving circuit; a glottal wave pulse modulator is used for amplitude modulating a glottal wave signal of normal speech of a patient and a carrier signal for exciting the ultrasonic transducer to generate a modulated signal; a differential output power amplifier is used for signal conditioning and power amplification of the modulated signal, and the amplified driving signal is output in a differential mode; a transformer is used for boosting the amplified driving signal to generate a boosted driving signal; and a matching circuit is used for realizing impedance matching between the boosted driving signal and the ultrasonic transducer, so that the power of the boosted driving signal is maximized and loaded on the ultrasonic transducer to drive the ultrasonic transducer to emit ultrasonic waves. The ultrasonic transducer driving circuit can reduce the overall volume of the driving circuit and meet the portable requirement of a handheld electronic artificial larynx device.