167 results about "Voice activity" patented technology

In some embodiments, a first audio signal is received via a first microphone, and a first probability of voice activity is determined based on the first audio signal. A second audio signal is received via a second microphone, and a second probability of voice activity is determined based on the first and second audio signals. Whether a first threshold of voice activity is met is determined based on the first and second probabilities of voice activity. In accordance with a determination that a first threshold of voice activity is met, it is determined that a voice onset has occurred, and an alert is transmitted to a processor based on the determination that the voice onset has occurred. In accordance with a determination that a first threshold of voice activity is not met, it is not determined that a voice onset has occurred.

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing speaker recognition, verification, and / or diarization. The techniques include processing audio data channels (ADCs) using a voice detection model to determine voice activity likelihoods (VALs) that individual ADCs include speech, obtaining, using VALs, a second set of ADC(s), and processing, using an audio processing a neural network (NN) model, the second set of ADCs to obtain association of the speech to the one or more speakers. The techniques also include generating a plurality of embeddings associated with the ADCs, processing the plurality of embeddings to obtain aggregated embedding(s) that represent audio data of multiple ADCs, and processing the aggregated embedding(s), using the audio processing NN model, to obtain association of the speech to the one or more speakers.

The invention provides a low-delay streaming voice interaction system with an interruption processing function, and relates to the technical field of artificial intelligence. Necessary preprocessing and acoustic feature extraction are performed through a real-time acoustic processing module, and distortion and complex environmental noise introduced by an interaction channel are resisted through a robustness enhancement technology; the streaming acoustic decoding module performs acoustic modeling, language model application and decoding in real time in parallel, and outputs an ultra-low-delay text transfer result stream; the real-time acoustic processing module is combined with a signal processing technology and is responsible for detecting user voice activity in a high-precision and ultra-low-delay manner, and particularly judging the real-time voice activity state of a user through the user voice activity in an AI voice playing period; an efficient and low-delay bidirectional streaming network transmission mode is adopted between modules of the system and between the modules and a communication platform, and it is ensured that audio streams, acoustic feature streams, text streams and control signals can be transmitted and processed in real time with extremely low end-to-end delay.

The invention relates to the technical field of earphone noise reduction processing, and discloses a motorcycle riding earphone noise reduction method combining different noise reduction algorithms, comprising the following steps: acquiring an original audio signal, and distributing the original audio signal to each processing module; a preprocessing signal is generated, voice activity information is determined, and environmental noise energy information is calculated; according to the environmental noise energy or the auxiliary information, adaptively adjusting a high-low noise energy threshold value; the original signals are input into the traditional and AI noise reduction module in parallel to generate two paths of noise reduction signals; based on the environment noise, the voice information and the threshold value, performing weighted fusion to generate an output signal; and carrying out equalization and compression processing on the output signal, and driving the loudspeaker to output. According to the invention, comprehensive judgment on environmental noise energy information, voice activity information and riding speed is introduced, an output signal of a traditional noise reduction module is set to be zero in a high-speed voice-free environment, and AI noise reduction is combined, so that tone quality distortion of a traditional noise reduction algorithm in a noise environment is effectively avoided.

The invention relates to a voice fraud analysis method, which comprises the following steps of: acquiring voice signals, respectively acquiring voice data from different sources through two independently configured microphone arrays, and recording the voice data into two independent and isolated audio channels; detecting the voice data in the two audio channels by using a voice activity detection technology, identifying and marking a voice activity time period in each audio channel, performing fragmentation operation on the voice data based on the voice activity time periods, and extracting a plurality of effective voice fragments; generating a dialogue text according to the extracted effective voice segments, and screening a text part to be analyzed from the generated text; and selecting a pre-trained fraud analysis model according to the data features of the to-be-analyzed text, and determining the fraud probability of the voice data through the analysis model. The method can achieve the precise processing and analysis of voice signals, remarkably improves the quality of voice data and the accuracy of fraud detection, and is suitable for a complex voice interaction scene.

The invention discloses a conference voice translation method and device and electronic equipment, the method is applied to the field of artificial intelligence, and the method comprises the steps: collecting original voice information in a conference through target equipment, and extracting an audio clip with sound activity from the original voice information through a voice activity detection model; performing voice recognition on the effective voice information in the audio clip with the sound activity to obtain first text information; and determining a to-be-translated target language type of the target user, and translating the first text information through the target translation model to obtain target text information. Through the conference recording method and device, the problem that the translated conference record is inaccurate due to the fact that traditional conference recording mainly depends on manual recording and translation and is easily influenced by subjective factors of a recorder in the prior art is solved.

The invention discloses a processing device and a voice activity detection system and method, and relates to the technical field of computers, the processing device comprises a feature extraction module and a voice activity detection module; the voice activity detection module is connected with the feature extraction module; the feature extraction module is used for continuously receiving audio signals, extracting feature information in the audio signals and sending the feature information and the audio signals to the voice activity detection module; and the voice activity detection module is used for performing voice activity detection on the audio signal based on the feature information, and sending the audio signal to the digital signal processor for processing when detecting that the voice activity exists in the audio signal. Compared with the prior art, the digital signal processor is controlled to be powered on under the condition of detecting that the voice activity exists in the audio signal, so that the digital signal processor can be kept in the power-off state in the detection process, power consumption can be saved to a large extent, and power consumption waste is avoided.

The invention provides a method and system for determining reply verbal skills of an outbound robot, and relates to the technical field of conversation robotics.The method comprises the steps that tone, volume and voice activity rate features of user voice signals are extracted, and voice feature vectors are generated; converting the voice signal into a text, generating a text semantic vector, and combining to generate a user state vector; generating a context semantic vector and an intention semantic vector based on a RoBERTa language model, calculating an intention correlation score, calculating an intention completion rate through a prediction model, generating a priority score in combination with an emergency degree, selecting a user intention according to a priority score function, training a reply model based on historical verbal skill data, and obtaining a reply result. The most appropriate recovery verbal skill is generated in real time, and the intelligence and adaptability of robot recovery are improved.

The invention relates to the field of real-time translation, and provides a real-time duplex translation method based on multichannel parallel processing and a corresponding product, and the method comprises the steps: collecting multipath voice signals of at least two user groups in real time through a group of audio collection modules; dynamically adjusting beam forming parameters of each audio acquisition module in one group of audio acquisition modules based on a sound source positioning result, and feeding back the beam forming parameters to the corresponding audio acquisition modules; monitoring the voice activity of each audio acquisition module corresponding to each audio channel; when it is monitored that the voice activity of any audio channel reaches a preset condition, automatically activating the translation processing flow of the audio channel and keeping the monitoring state of the other audio channels; a parallel processing mechanism is adopted for voice signals of the activated audio channels, and meanwhile real-time translation of the currently activated audio channels and voice activity monitoring of the other audio channels are executed; and transmitting a translation result of the current speaking user to other users participating in dialogue in the user group to realize synchronous coordination of multichannel data.

The invention relates to the technical field of wireless communication, in particular to a Bluetooth mesh-based riding helmet earphone multi-terminal talkback synchronization method and system, and the method comprises the steps: constructing a self-organizing network through a plurality of terminal nodes, carrying out the error detection of the self-organizing network, obtaining an error value, carrying out the time compensation of the terminal nodes, and obtaining a calibration terminal node, carrying out voice activity monitoring on the plurality of calibration terminal nodes to obtain a plurality of marked terminal nodes, carrying out time slot allocation on the plurality of ordered voice requests to obtain a plurality of voice frames with timestamps, carrying out multi-hop forwarding on the plurality of voice frames with timestamps to obtain a plurality of verification voice frames, carrying out timestamp sorting to obtain a plurality of sorted voice frames, and sending the sorted voice frames to a server; and performing synchronous error correction to obtain a plurality of synchronous voice frames, decoding and playing the plurality of synchronous voice frames to obtain a synchronous voice stream, and realizing multi-terminal synchronous talkback. According to the invention, the problems of difficult network establishment, asynchronous voice and speaking right conflict in the application of the riding helmet Bluetooth earphone can be solved.

Methods, apparatus and systems for radio-based voice activity detection are described. In one example, a described system comprises: a transmitter configured to transmit a radio signal through a wireless channel of a venue; a receiver configured to receive the radio signal through the wireless channel, wherein the wireless channel is impacted by a voice activity of a target voice source in the venue; and a processor. The processor is configured for: computing a time series of channel information (CI) of the wireless channel based on the radio signal, and detecting the voice activity of the target voice source based on the time series of CI (TSCI) of the wireless channel, without using any media signal.

The invention discloses a human voice recognition method and system based on multi-person conference records, and relates to the technical field of human voice recognition, and the method comprises the steps: collecting multi-person conference audio signals through a microphone based on a multi-person conference; framing processing is carried out according to the audio signals, and the short-time energy and the zero crossing rate of the audio signals in the conference are calculated. According to the method, through filtering processing, background noise can be remarkably suppressed, the definition and the discernibility of voice signals are improved, voice activities are analyzed through framing processing, short-time energy calculation and zero crossing rate calculation on audio signals, the accuracy and the efficiency of voice recognition and processing in conference records can be remarkably improved, and the voice recognition and processing efficiency is improved. Time domain signals can be accurately converted to a frequency domain through FFT, basic attributes of voice signals are accurately captured, overlapped voices of the voice signals are further recognized and classified in combination with data collection of a multi-microphone array, conference recognition accuracy is improved, and semantic features are combined.

The invention provides a data processing method and device based on a large model, equipment and a storage medium, and relates to the technical field of data processing, in particular to the technical fields of artificial intelligence, big data, large models and the like. The specific implementation scheme is as follows: performing voice activity detection on a target dialogue audio to obtain a plurality of target audio clips with voice activities; obtaining target text content based on the plurality of target audio clips; performing intention extraction on the target text content by utilizing a large model, and extracting at least one of a question text related to a question and an answer text related to an answer; based on the time information in the target dialogue audio and the extracted text, a target dialogue text with the time information associated with the target dialogue audio is obtained, and the target dialogue text comprises at least one of a question and answer pair with the time information and a question text with the time information.

To provide a care information generation system which recognizes required voice from voice of a care site and can generate care information.SOLUTION: A care information generation system 1000 includes a generative AI engine G and at least one of terminal devices 10A and 10B for generating care information from voice of a care site. The terminal devices A and B include microphones 11A and 11B for converting voice of the care site into a voice signal, recognize generation of voice activity data based on the voice signal and transmit the voice activity data to the generative AI engine. The voice activity data received from the terminal devices is inputted to the generative AI engine G and the generative AI engine G generates care information based on the voice activity data.SELECTED DRAWING: Figure 1

An illustrative hearing system may be configured to receive, from an input transducer included in a hearing device configured to be worn by a user, audio data representative of one or more audio signals presented to the user and acquire motion data representative of head movements of the user while the user wears the hearing device and / or own-voice data representative of an own-voice activity of the user. The hearing system may be further configured to determine, based the motion data and / or own-voice data, a listening state of the user with respect to the one or more audio signals and perform, based on the listening state, an operation associated with the hearing device.

The embodiment of the invention provides a multi-channel adaptive noise suppression method and device, and the method comprises the steps: collecting audio data in a current environment through a preset microphone array; processing the audio data through an adaptive filtering algorithm and a human voice active interval marking model, and determining a noise reference signal and a human voice active interval; based on the noise reference signal and the human voice active interval, determining an inhibition interval and corresponding noise inhibition intensity; and based on the suppression interval and the corresponding noise suppression intensity, performing suppression processing on the sound data in the current environment, and outputting noise-reduced audio data. Through the adaptive filtering and the human voice active interval marking model, the environmental audio data is processed, the noise is suppressed, and the denoised audio data is output, so that the interference of the environmental noise on the audio data is reduced, and the accuracy of sound processing and the noise reduction capability are improved.

This application discloses an intelligent method and apparatus for detecting active speech with sentence segmentation based on multi-state temporal modeling. The method includes: receiving audio signals from at least one channel; extracting acoustic feature sequences from the audio signals using a target speech recognition model corresponding to the number of channels; determining the probability distribution of each speech frame corresponding to the acoustic feature sequences belonging to different speech activity states, obtaining a state sequence corresponding to each channel, wherein the speech activity state includes at least one of the following: initial silence state, speech state, intra-turn pause silence state, and inter-turn sentence segmentation silence state; and determining the time of sentence segmentation in the audio signal based on the state sequence. This application solves the technical problem of erroneous sentence segmentation in speech activity detection based on a fixed silence threshold in related technologies.

The invention discloses a voice noise reduction method and device for a multi-person scene, electronic equipment and a storage medium, and relates to the technical field of voice signal processing. The method comprises the following steps: acquiring an audio signal and a video image of a target space scene; determining audio perception information matched with the audio signal and a visual voice activity detection result corresponding to the video image; fusing the audio perception information and the visual voice activity detection result, and determining a target voice sounding object when the voice activity fusion result indicates that the voice activity exists; and positioning a human face corresponding to the target voice sounding object to obtain position change information, updating a pickup direction, controlling beam forming processing, and outputting a denoised target voice signal. According to the scheme provided by the invention, the voice of the current effective speaker can be accurately separated and enhanced from the aliasing audio signals, meanwhile, the interference of other speakers and environmental noise is inhibited, and help is provided for realizing high-quality voice interaction and recognition.

The invention relates to the technical field of speech recognition and synthesis, and discloses a multi-mode speech conversion method and system based on user behaviors, and the method comprises the steps: carrying out the vibration separation of a bone conduction signal, obtaining a separation vibration signal, mapping the face muscle micro-current into a muscle deformation gradient, and analyzing the intention intensity of a user through implicit behavior data; mapping the muscle deformation gradient into a voice fundamental frequency of the user, linearly converting the separation vibration signal into a voice formant of the user, and performing noise injection on a non-voice active section based on intention intensity to obtain noise injection voice; constructing user behavior voice corresponding to the voice fundamental frequency, the voice formant and the noise injection voice through an orthogonal projection layer in a preset orthogonal regularization vocoder; and synthesizing the speech feature vector and the user behavior speech into bone conduction propagation speech through a bone conduction synthesis layer in an orthogonal regularization vocoder. According to the invention, the accuracy of the multi-mode voice conversion technology can be improved.

The invention provides a multi-mode voice interaction method and device and intelligent equipment, is suitable for the technical field of intelligent voice interaction, is applied to the intelligent equipment, and comprises the following steps: in response to a detected voice activity, extracting first voice data in the voice activity, and obtaining video data shot synchronously with the first voice data, a plurality of different users are shot in the video data. And screening out a target user sending the first voice data from the video data. And performing semantic integrity analysis on the text content corresponding to the first voice data. And when the semantic integrity analysis result is that the semantics is incomplete, continuously acquiring second voice data of the target user for a plurality of times, and generating a corresponding target statement with complete semantics after the first voice data and the second voice data are combined. Generating reply data according to the target statement, and outputting the reply data through voice. According to the embodiment of the invention, accurate, coherent and real-time voice interaction with the user needing interaction can be realized.

The invention discloses a full-duplex intelligent voice interaction system based on voice activity detection and intention recognition, and the system comprises a voice recognition module which is used for converting a voice stream into a text; the voice activity detection module is used for detecting voice activity; the intention recognition module is used for judging whether the user voice has a clear intention or not; the interruption control module is used for controlling pause and recovery of voice broadcast according to the voice activity detection result and the intention recognition result; the speech synthesis module is used for converting the text into a speech stream; the buffer area management module is used for managing a voice stream buffer area; and the resource management module is used for managing different tasks through the thread pool and processing voice recognition, voice activity detection and voice synthesis tasks in parallel. The invention further provides a full-duplex intelligent voice interaction method based on voice activity detection and intention recognition. Full-duplex voice interaction is realized; a mode of combining voice activity detection and intention recognition is adopted; and a high-concurrency scene is supported.

The invention provides a voice activity detection method and related equipment. The method comprises the following steps: acquiring an audio signal to be processed, and determining a voice activity sequence of the audio signal based on a diffusion model; wherein the processing of the audio signal based on the diffusion model comprises the following steps: obtaining a time frame signal in a sequence form according to audio features, calculating a gradient of the time frame signal based on a neural network model, predicting a sampling signal according to the gradient, and obtaining a voice activity sequence for indicating whether a voice signal exists in each time frame based on the sampling signal. Compared with a related detection method, the method focuses on probability distribution of voice signals, is not affected by the signal-to-noise ratio, and can work normally under the extremely low signal-to-noise ratio; noise signals are not concerned, influence of noise types is avoided, and generalization performance is better. Therefore, an accurate voice activity detection result can be stably obtained.

A method for audio processing performed by an electronic device is provided. The method includes detecting at least one of a first voice activity near a first device and a second voice activity near a second device using a voice recognition module associated with the first device, comparing the first voice activity and the second voice activity to determine whether the first voice activity and the second voice activity exceed a predetermined threshold, and outputting the at least one of the first voice activity or the second voice activity through the second device in response to determining that the first voice activity and the second voice activity exceed the predetermined threshold.

The application relates to the field of real-time translation, and provides a real-time duplex translation method based on multi-channel parallel processing and a corresponding product.The method comprises the following steps: collecting multi-channel voice signals of at least two user groups in real time through a group of audio acquisition modules respectively; dynamically adjusting beam forming parameters of each audio acquisition module in the group of audio acquisition modules based on a sound source positioning result and feeding back to the corresponding audio acquisition module; monitoring voice activity of each audio channel corresponding to each audio acquisition module; when the voice activity of any audio channel reaches a predetermined condition, automatically activating a translation processing procedure of the audio channel and keeping the remaining audio channels in a monitoring state; using a parallel processing mechanism for the voice signals of the activated audio channel, simultaneously performing real-time translation of the currently activated audio channel and voice activity monitoring of the remaining audio channels; and transmitting the translation result of the current speaker to other users participating in the conversation in the user group, so that multi-channel data is synchronously coordinated.

This invention discloses a safe driving voice and visual interaction control method. The method includes: synchronously collecting and preprocessing the driver's visual data and voice interaction data; extracting key visual features based on the preprocessed visual data and calculating visual state feature values; triggering standardized voice interaction based on the visual state feature values, calculating a voice activity score in conjunction with the preprocessed voice interaction data, and determining the driver's voice response delay level based on the voice activity score; matching based on a predefined 3D virtual guide action sequence according to the voice response delay level, triggering a linkage response after matching to form a non-intrusive driving reminder with light, sound, and shape linkage; and completing closed-loop feedback control based on the execution state of the 3D virtual guide action sequence and the non-intrusive driving reminder with light, sound, and shape linkage. The method provided by this invention can reduce the monitoring misjudgment rate and achieve non-intrusive safety reminders.

The present disclosure discloses a speech data processing method and device, electronic equipment and storage medium. The method comprises: obtaining target speech data to be processed; detecting the target speech data to obtain target phonemes corresponding to each audio frame; determining the target type of the audio frame corresponding to the target phoneme based on the phoneme type corresponding to the target phoneme, wherein the target type includes a silence type or a non-silence type; determining the voice activity boundary in the target speech data based on the target type corresponding to the audio frame, and taking the voice activity boundary as the detection result of the target speech data. The method provided by the present disclosure can more accurately detect the type of audio frame by identifying the phonemes of each audio frame in the speech data and determining the silence type audio frame and the non-silence type audio frame using the phonemes, and can more accurately locate the voice activity boundary in the speech data compared with the existing technology using the two-classification method.

Audio systems, methods, and processor instructions are provided herein that detect voice activity of a user and provide an output voice signal. The systems, methods, and instructions receive a plurality of microphone signals and combine the plurality of microphone signals according to a first combination and a second combination. The first combination produces a primary signal having an enhanced response in a direction of a mouth of the user, and the second combination produces a reference signal having a reduced response in the direction of the mouth of the user. The primary signal and the reference signal are added and subtracted to produce a sum signal and a difference signal, respectively. The sum signal is compared to the difference signal and an output voice signal is provided based on the comparison.

A method for audio processing performed by an electronic device is provided. The method includes detecting at least one of a first voice activity near a first device and a second voice activity near a second device using a voice recognition module associated with the first device, comparing the first voice activity and the second voice activity to determine whether the first voice activity and the second voice activity exceed a predetermined threshold, and outputting the at least one of the first voice activity or the second voice activity through the second device in response to determining that the first voice activity and the second voice activity exceed the predetermined threshold.

A microphone transmitter has a microcontroller that operates in a high gain mode when the portable housing is in a horizontal position or in a low gain mode when the portable housing is in a vertical position. In another implementation, the microcontroller operates in one of the following modes: (1) operate the microphone at a low microphone gain when the portable housing is in a vertical position and when voice activity is detected; (2) operate the microphone at a lower microphone gain when the portable housing is in a vertical position and when no voice activity is detected; (3) operate the microphone at a high microphone gain when the portable housing is in a horizontal position and when no voice activity is detected; or (4) operate the microphone at a higher microphone gain when the portable housing is in a horizontal position and when voice activity is detected.

The invention relates to the technical field of voice activity recognition, and discloses a voice activity detection method and device based on threshold self-adaption. The method comprises the following steps: performing beam forming processing on an original multi-channel signal picked up by an array microphone to obtain a voice beam and a noise beam; respectively calculating a long-time energy difference and a short-time energy difference at each moment according to the voice beam and the noise beam; performing first voice activity detection according to the long-time energy difference of each moment and a first threshold value; if the first voice activity detection result is a to-be-detected signal, performing second voice activity detection in each scanning window according to the short-time energy difference at each moment; wherein the second voice activity detection comprises peak value updating, peak value attenuation value updating, threshold value updating and voice activity judgment. According to the method, the judgment threshold value can be updated in real time, the voice and the noise can be rapidly and accurately detected in a complex environment, rapid noise tracking is achieved, and the accuracy of voice activity detection is improved.