TWI643184B - Method and apparatus for speaker diarization - Google Patents

Abstract

The invention relates to a method and a device for voice segmentation. The method for voice segmentation comprises: when receiving a mixed voice sent by a terminal, the automatic answering system divides the mixed voice into a plurality of phrase segments, and each phrase sound a segment identifier corresponding to the speaker identifier; using a time recurrent neural network to establish a voiceprint model for the phrase segment corresponding to each speaker identifier, and adjusting a corresponding segmentation boundary in the mixed voice based on the voiceprint model to segment each The valid voice segment corresponding to the speaker identification. The invention can effectively improve the precision of the speech segmentation, especially for the speech alternating frequently, and the overlapping speech, the speech segmentation effect is better.

Description

 Method and device for voice segmentation  

The present invention relates to the field of voice processing technologies, and in particular, to a method and apparatus for voice segmentation.

At present, many voices received by the call center are mixed with voices of many people. In this case, speaker diarization is required to further perform voice analysis on the target voice. Speech segmentation means that in the field of speech processing, when the speeches of multiple speakers are combined and recorded in one channel, the speech of each speaker in the signal is separately extracted. The traditional speech segmentation technology is based on the global background model and the Gaussian mixture model. Due to the limitation of technology, the segmentation accuracy of this segmentation method is not high, especially for dialogues with frequent alternating and overlapping dialogue segmentation effects. difference.

It is an object of the present invention to provide a method and apparatus for speech segmentation, which aims to effectively improve the accuracy of speech segmentation.

To achieve the above object, the present invention provides a method for voice segmentation, characterized in that: the method for voice segmentation comprises: S1, the automatic response system divides the mixed voice into multiples when receiving the mixed voice sent by the terminal a phrase segment, and marking a corresponding speaker identifier for each phrase segment; S2, establishing a voiceprint model for the phrase segment corresponding to each speaker identifier by using a time recurrent neural network, and adjusting the mixture based on the voiceprint model Corresponding segmentation boundaries in the speech to segment the valid speech segments corresponding to each speaker identifier.

In an embodiment, the step S1 includes: S11, acquiring a silent segment in the mixed voice, and removing a silent segment in the mixed voice, to segment the mixed voice according to the silent segment, and obtain a segmentation. a long speech segment; S12, framing the long speech segment to extract acoustic features of each long speech segment; S13, performing KL distance analysis on acoustic features of each long speech segment, according to KL distance analysis results Segmenting the speech segment to obtain a segmented phrase segment; S14, performing a speech clustering on each phrase segment by using a Gaussian mixture model, and marking a corresponding speaker identifier for the phrase segment of the same speech class.

In an embodiment, the step S13 includes: performing KL distance analysis on the acoustic features of each long speech segment, and segmenting the long speech segment whose duration is greater than the preset time threshold at a maximum value of the KL distance. The segmented phrase segment.

In an embodiment, the step S2 includes: S21, using the time recurrent neural network to establish a voiceprint model for a phrase segment corresponding to each speaker identifier, and extracting a characterizing feature of the speaker based on the voiceprint model. a preset type vector; S22, calculating a maximum posterior probability of the speaker corresponding to each voice frame based on the preset type vector; S23, adjusting the mixed Gaussian of the speaker based on the maximum posterior probability and using a predetermined algorithm a model; S24, acquiring a speaker with the highest probability corresponding to each voice frame based on the adjusted mixed Gaussian model, and adjusting a corresponding segmentation boundary in the mixed voice according to a probability relationship between the speaker with the highest probability and the voice frame; S25, The voiceprint model is updated n times, and the mixed Gaussian model is iterated m times each time the voiceprint model is updated to obtain an effective voice segment corresponding to each speaker, and both n and m are greater than 1 Integer.

In an embodiment, after the step S2, the method further includes: acquiring a corresponding response content based on the valid voice segment, and feeding back the response content to the terminal.

In order to achieve the above object, the present invention further provides a device for voice segmentation, the device for voice segmentation comprising: a segmentation module, configured to divide the mixed speech into a plurality of phrase sounds when receiving the mixed speech sent by the terminal And segmenting the corresponding speaker identifier for each phrase segment; the adjustment module is configured to establish a voiceprint model for the phrase segment corresponding to each speaker identifier by using a time recurrent neural network, and adjust the voiceprint model based on the voiceprint model The corresponding segmentation boundary in the mixed speech is segmented to segment the valid segment of speech corresponding to each speaker identifier.

In an embodiment, the splitting module includes: a removing unit, configured to acquire a silent segment in the mixed voice, and remove a silent segment in the mixed voice, to perform the mixed voice according to the silent segment. Dividing to obtain a divided long speech segment; a framing unit for framing the long speech segment to extract an acoustic feature of each long speech segment; and a segmentation unit for each long speech segment The acoustic feature is analyzed by KL distance, and the segment is segmented according to the KL distance analysis result to obtain a segmented phrase segment; the clustering unit is used for performing speech clustering on each phrase segment by using a Gaussian mixture model. The corresponding speaker identifier is marked with the phrase segment of the same voice class.

In an embodiment, the segmentation unit is specifically configured to perform KL distance analysis on the acoustic features of each long speech segment, and segment the long speech segment whose duration is greater than the preset time threshold at a maximum value of the KL distance. , get the segmented phrase segment.

In an embodiment, the adjustment module includes: a modeling unit, configured to establish a voiceprint model for a phrase segment corresponding to each speaker identifier by using the time recurrent neural network, and extract a representation based on the voiceprint model a preset type vector of the speaker identity feature; the calculating unit is configured to calculate a maximum posterior probability of the speaker corresponding to each voice frame genus based on the preset type vector; the first adjusting unit is configured to be based on the maximum Checking the rate and adjusting the mixed Gaussian model of the speaker by using a predetermined algorithm; the second adjusting unit is configured to acquire the speaker with the highest probability corresponding to each voice frame based on the adjusted mixed Gaussian model, and according to the speaker with the highest probability The probability relationship of the speech frame adjusts a corresponding segmentation boundary in the mixed speech; an iteration unit is used to update the voiceprint model n times in an iterative manner, and the hybrid Gauss is iterated m times each time the voiceprint model is updated The model is to obtain a valid speech segment corresponding to each speaker, and both n and m are positive integers greater than one.

In an embodiment, the apparatus for voice segmentation further includes: a feedback module, configured to acquire a corresponding response content based on the valid voice segment, and feed back the response content to the terminal.

The invention has the beneficial effects that the present invention first divides the mixed speech into a plurality of phrase segments, each phrase segment correspondingly identifies a speaker, and uses a time recurrent neural network to establish a voiceprint model for each phrase segment. Since the voiceprint model established by using the time recurrent neural network can correlate the sound information of the speaker across time points, the adjustment of the segmentation boundary of the phrase segment can be realized based on the voiceprint model, which can effectively improve the accuracy of the voice segmentation, especially It is a good effect for speech segmentation for speech that alternates frequently and has overlapping speech.

101‧‧‧Segment Module

1011‧‧‧Removal unit

1012‧‧‧Frame unit

1013‧‧‧Segment unit

1014‧‧‧ clustering unit

102‧‧‧Adjustment module

1021‧‧‧Modeling unit

1022‧‧‧Computation unit

1023‧‧‧First adjustment unit

1024‧‧‧Second adjustment unit

1025‧‧‧ iteration unit

S1‧‧‧ steps

S11‧‧ steps

Step S12‧‧‧

S13‧‧‧ steps

S14‧‧‧ steps

S2‧‧‧ steps

S21‧‧‧ steps

S22‧‧‧ steps

S23‧‧‧Steps

S24‧‧‧Steps

S25‧‧‧ steps

FIG. 1 is a schematic flow chart of a method for voice segmentation according to an embodiment of the present invention.

FIG. 2 is a schematic diagram showing the refinement process of step S1 shown in FIG. 1.

FIG. 3 is a schematic diagram of the refinement process of step S2 shown in FIG. 1.

FIG. 4 is a schematic structural diagram of an apparatus for voice segmentation according to an embodiment of the present invention.

FIG. 5 is a schematic structural view of the split module shown in FIG. 4.

FIG. 6 is a schematic structural view of the adjustment module shown in FIG. 4.

The principles and features of the present invention are described in the following with reference to the accompanying drawings.

As shown in FIG. 1 , FIG. 1 is a schematic flowchart of a method for voice segmentation according to an embodiment of the present invention. The method for voice segmentation includes the following steps: Step S1: When an automatic answering system receives a mixed voice sent by a terminal, The mixed voice is divided into a plurality of phrase segments, and each speaker segment is marked with a corresponding speaker identifier; in this embodiment, it can be applied to an automatic answering system of a call center, such as an automatic answering system of an insurance call center, and various customer service calls. The center's automatic answering system and more. The automatic answering system receives the original mixed voice sent by the terminal, and the mixed voice is mixed with sounds generated by a plurality of different sound sources, such as a sound mixed by a plurality of people, a sound mixed by a plurality of people and a sound mixed with other noises, and the like. .

In this embodiment, the mixed voice can be segmented into a plurality of phrase segments by using a predetermined method. For example, a Gaussian Mixture Model (GMM) can be used to divide the mixed voice into a plurality of phrase segments. Of course, other traditions can also be utilized. The method divides the mixed speech into multiple phrase segments.

Wherein, after the speech segmentation of the embodiment, each phrase segment should correspond to only one speaker, and different phrase segments may have the same speaker, and different speaker sounds of the same speaker. The segments are labeled the same.

Step S2, using a time recurrent neural network to establish a voiceprint model for the phrase segments corresponding to each speaker identifier, and adjusting a corresponding segmentation boundary in the mixed voice based on the voiceprint model to segment the corresponding speaker identifiers. Effective voice segment.

In this embodiment, the Long-Short Term Memory (LSTM) model has a directional loop introduced by a recurrent neural network in a traditional forward feedback neural network to process before and after inter-layer input and before and after layer output. The association. By modeling the speech sequence with the time recurrent neural network, the speech signal characteristics across time points can be obtained, which can be used to process the speech sequence with associated information at any length and any position. The time recurrent neural network model can design the multiple interaction layers in the neural network layer to memorize the information on the nodes at the farther time. In the time recurrent neural network model, the “forget gate layer” discards the identification task. Information, then use the "input gate layer" to determine the state that needs to be updated, and finally determine the state that needs to be output and process the output.

In this embodiment, a voice pattern is established by using a time recurrent neural network for a phrase segment corresponding to each speaker identifier, and the voice information of the speaker crossing the time point can be obtained by the voiceprint model, and the mixed voice can be adjusted based on the voice information. Corresponding segmentation boundaries are used to adjust the segmentation boundaries of all the phrase segments corresponding to each speaker, and finally the effective segment of speech corresponding to each speaker identifier is segmented, and the effective segment can be regarded as the complete speech of the corresponding speaker.

Compared with the prior art, the embodiment first divides the mixed speech into a plurality of phrase segments, and each phrase segment corresponds to identify a speaker, and uses a time recurrent neural network to establish a voiceprint model for each phrase segment. Since the voiceprint model established by using the time recurrent neural network can correlate the sound information of the speaker across time points, the adjustment of the segmentation boundary of the phrase segment can be realized based on the voiceprint model, which can effectively improve the accuracy of the voice segmentation, especially It is a good effect for speech segmentation for speech that alternates frequently and has overlapping speech.

In a preferred embodiment, as shown in FIG. 2, on the basis of the foregoing embodiment of FIG. 1, the foregoing step S1 includes: Step S11, acquiring a silent segment in the mixed voice, and removing the mixed voice. a silent segment to segment the mixed speech according to the silent segment to obtain a segmented long speech segment; and in step S12, segment the long speech segment to extract an acoustic feature of each long speech segment; Step S13, performing KL distance analysis on the acoustic features of each long speech segment, segmenting the speech segment according to the KL distance analysis result, and obtaining the segmented phrase segment, step S14, using the Gaussian mixture model for each short The speech segment performs speech clustering, and the corresponding speaker segment is marked with the phrase segment of the same speech class.

In this embodiment, the initial segmentation is first performed according to the mute: determining the mute segment in the mixed speech, and removing the determined mute segment from the mixed speech, so as to realize the segmentation of the mixed speech according to the mute segment, and the mute segment is through the mixed speech. The analysis of short-term speech energy and short-term zero-crossing rate is determined.

After removing the silent segment, first assume that in the entire mixed voice, each person speaks for a fixed threshold Tu. If a certain voice is greater than the duration, more people may speak. If it is less than the duration, it is more likely to be only One person speaks, based on this assumption, an inter-frame KL distance analysis can be performed on the acoustic features of the speech segment of each of the long speech segments after the silence segmentation is greater than the fixed threshold Tu. Of course, inter-frame KL distance analysis can also be performed on the acoustic characteristics of all long speech segments. Specifically, the obtained long speech segment is framed to obtain a speech frame of each long speech segment, the acoustic features of the speech frame are extracted, and KL distance (ie, relative entropy) is analyzed for the acoustic features of all the long speech segments. Among them, the acoustic characteristics include, but are not limited to, a linear prediction coefficient, a cepstral coefficient MFCC, an average zero-crossing rate, a short-time spectrum, a formant frequency, and a bandwidth.

Among them, the meaning of KL distance analysis is that for two discrete types of acoustic feature probability distribution sets P = {p1, p2, ..., pn} and Q = {q1, q2, ..., qn}, P and Q KL distance between: When the KL distance is larger, the difference between the two PQs is greater, that is, the two sets of PQ are from the voices of two different people. Preferably, the long speech segment whose duration is greater than the preset time threshold is segmented at the maximum value of KL to improve the accuracy of speech segmentation.

The long speech segment is segmented to obtain a phrase segment, and the number of phrase segments is greater than the number of long segments. Then, the phrase segment clustering is performed: clustering the segmented phrase segments to aggregate all the phrase segments into a plurality of phonetic classes, and labeling the corresponding speaker segments for each phrase segment, wherein, the same The phonetic phrase segment is labeled with the same speaker identifier, and the phrase segments that are not of the same voice class are labeled with different speaker identifiers. The clustering method is: using the Gaussian mixture model of K components to fit each segment of the phrase, using the mean as the feature vector, and using k-means clustering method to cluster all the phrase segments into multiple categories.

In a preferred embodiment, as shown in FIG. 3, based on the foregoing embodiment, the step S2 includes: Step S21, using the time recurrent neural network to identify a phrase segment corresponding to each speaker. Establishing a voiceprint model, and extracting a preset type vector representing the speaker identity feature based on the voiceprint model; and step S22, calculating a maximum posterior probability of the speaker corresponding to each voice frame based on the preset type vector; S23, adjusting the mixed Gaussian model of the speaker by using a predetermined algorithm based on the maximum posterior probability; and step S24, acquiring the speaker with the highest probability corresponding to each voice frame based on the adjusted mixed Gaussian model, and according to the maximum probability The probability relationship between the speaker and the voice frame adjusts the corresponding segmentation boundary in the mixed voice; in step S25, the voiceprint model is updated n times, and the hybrid Gauss is iterated m times each time the voiceprint model is updated. The model is to obtain a valid speech segment corresponding to each speaker, and both n and m are positive integers greater than one.

In this embodiment, a temporal recursive neural network is used to establish a voiceprint model for a phrase segment corresponding to each speaker identifier, and a preset type vector that characterizes the speaker identity feature is extracted based on the voiceprint model. Preferably, the pre-preparation Let the type vector be an i-vector vector, which is an important feature that reflects the speaker's acoustic difference.

In the whole mixed speech, the maximum posterior probability of each speaker is calculated according to the preset type vector, and the mixed Gaussian model of the speaker is re-adjusted by the preset algorithm in the mixed speech by calculating the maximum posterior probability. For example, the speaker's mixed Gaussian model is re-adjusted by the Baum-Welch algorithm, which is a set of k (typically 3-5) Gaussian models. Use the re-adjusted mixed Gaussian model to find the speaker with the highest probability of each speech frame. The segmentation boundary of the mixed speech is adjusted according to the probability relationship between the speech frame and the found speaker, for example, the segmentation boundary is fine-tuned forward or fine-tuned backward. Finally, the above-mentioned voiceprint model is updated n times, and each time the voiceprint model is updated, the m-mixed Gaussian model is superimposed to obtain an effective speech segment corresponding to each speaker, and n and m are positive integers greater than 1.

In this embodiment, a voiceprint model is established by means of a deep learning time recurrent neural network, and each voice frame corresponding to each speaker voice pattern is used to calculate a probability that the voice frame belongs to a certain speaker, and the probability correction model is finally adjusted. The boundary of speech segmentation can effectively improve the accuracy of speaker speech segmentation, reduce the error rate, and have good scalability.

In a preferred embodiment, based on the foregoing embodiment, after the step S2, the method further includes: acquiring a corresponding response content based on the valid voice segment, and feeding back the response content to the terminal.

In this embodiment, the automatic response system is associated with a corresponding response library, and the response library stores response content corresponding to different questions. After receiving the mixed voice sent by the terminal, the automatic response system divides the response into a speaker identifier. An effective voice segment, from which an effective voice segment related to the automatic answering system is obtained, and the valid voice segment is matched in the response library, and the matched response content is fed back to the terminal.

As shown in FIG. 4, FIG. 4 is a schematic structural diagram of an apparatus for voice segmentation according to an embodiment of the present invention. The apparatus for voice segmentation includes: a segmentation module 101, configured to: when the mixed voice sent by the terminal is received, the hybrid The voice is divided into a plurality of phrase segments, and each speaker segment is marked with a corresponding speaker identifier; the voice segmentation device of the embodiment includes an automatic response system, such as an automatic answering system of an insurance call center, and various customer service call centers. Auto answering system and more. The automatic answering system receives the original mixed voice sent by the terminal, and the mixed voice is mixed with sounds generated by a plurality of different sound sources, such as a sound mixed by a plurality of people, a sound mixed by a plurality of people and a sound mixed with other noises, and the like. .

In this embodiment, the mixed voice can be segmented into a plurality of phrase segments by using a predetermined method. For example, a Gaussian Mixture Model (GMM) can be used to divide the mixed voice into a plurality of phrase segments. Of course, other traditions can also be utilized. The method divides the mixed speech into multiple phrase segments.

Wherein, after the speech segmentation of the embodiment, each phrase segment should correspond to only one speaker, and different phrase segments may have the same speaker, and different speaker sounds of the same speaker. The segments are labeled the same.

The adjustment module 102 is configured to establish a voiceprint model for a phrase segment corresponding to each speaker identifier by using a time recurrent neural network, and adjust a corresponding segmentation boundary in the mixed voice based on the voiceprint model to segment each speech The person identifies the corresponding valid voice segment.

In this embodiment, the Long-Short Term Memory (LSTM) model has a directional loop introduced by a recurrent neural network in a traditional forward feedback neural network to process before and after inter-layer input and before and after layer output. The association. By modeling the speech sequence with the time recurrent neural network, the speech signal characteristics across time points can be obtained, which can be used to process the speech sequence with associated information at any length and any position. The time recurrent neural network model can design the multiple interaction layers in the neural network layer to memorize the information on the nodes at the farther time. In the time recurrent neural network model, the “forget gate layer” discards the identification task. Information, then use the "input gate layer" to determine the state that needs to be updated, and finally determine the state that needs to be output and process the output.

In this embodiment, a voice pattern is established by using a time recurrent neural network for a phrase segment corresponding to each speaker identifier, and the voice information of the speaker crossing the time point can be obtained by the voiceprint model, and the mixed voice can be adjusted based on the voice information. Corresponding segmentation boundaries are used to adjust the segmentation boundaries of all the phrase segments corresponding to each speaker, and finally the effective segment of speech corresponding to each speaker identifier is segmented, and the effective segment can be regarded as the complete speech of the corresponding speaker.

In a preferred embodiment, as shown in FIG. 5, on the basis of the foregoing embodiment of FIG. 4, the splitting module 101 includes: a removing unit 1011, configured to acquire a silent segment in the mixed voice, and remove a mute segment in the mixed voice to segment the mixed speech according to the mute segment to obtain a segmented long speech segment; a framing unit 1012, configured to frame the long speech segment to extract An acoustic feature of each long speech segment; a segmentation unit 1013 configured to perform KL distance analysis on the acoustic features of each long speech segment, and segment the speech segment according to the KL distance analysis result to obtain a short after segmentation a speech segment; a clustering unit 1014, configured to perform speech clustering on each phrase segment by using a Gaussian mixture model, and label a corresponding speaker segment with a phrase segment of the same phonetic class.

In this embodiment, the initial segmentation is first performed according to the mute: determining the mute segment in the mixed speech, and removing the determined mute segment from the mixed speech, so as to realize the segmentation of the mixed speech according to the mute segment, and the mute segment is through the mixed speech. The analysis of short-term speech energy and short-term zero-crossing rate is determined.

After removing the silent segment, first assume that in the entire mixed voice, each person speaks for a fixed threshold Tu. If a certain voice is greater than the duration, more people may speak. If it is less than the duration, it is more likely to be only One person speaks, based on this assumption, an inter-frame KL distance analysis can be performed on the acoustic features of the speech segment of each of the long speech segments after the silence segmentation is greater than the fixed threshold Tu. Of course, inter-frame KL distance analysis can also be performed on the acoustic characteristics of all long speech segments. Specifically, the obtained long speech segment is framed to obtain a speech frame of each long speech segment, the acoustic features of the speech frame are extracted, and KL distance (ie, relative entropy) is analyzed for the acoustic features of all the long speech segments. Among them, the acoustic characteristics include, but are not limited to, a linear prediction coefficient, a cepstral coefficient MFCC, an average zero-crossing rate, a short-time spectrum, a formant frequency, and a bandwidth.

Among them, the meaning of KL distance analysis is that for two discrete types of acoustic feature probability distribution sets P = {p1, p2, ..., pn} and Q = {q1, q2, ..., qn}, P and Q KL distance between: When the KL distance is larger, the difference between the two PQs is greater, that is, the two sets of PQ are from the voices of two different people. Preferably, the long speech segment whose duration is greater than the preset time threshold is segmented at the maximum value of KL to improve the accuracy of speech segmentation.

The long speech segment is segmented to obtain a phrase segment, and the number of phrase segments is greater than the number of long segments. Then, the phrase segment clustering is performed: clustering the segmented phrase segments to aggregate all the phrase segments into a plurality of phonetic classes, and labeling the corresponding speaker segments for each phrase segment, wherein, the same The phonetic phrase segment is labeled with the same speaker identifier, and the phrase segments that are not of the same voice class are labeled with different speaker identifiers. The clustering method is: using the Gaussian mixture model of K components to fit each segment of the phrase, using the mean as the feature vector, and using k-means clustering method to cluster all the phrase segments into multiple categories.

In a preferred embodiment, as shown in FIG. 6, on the basis of the foregoing embodiment, the adjustment module 102 includes: a modeling unit 1021, configured to identify each speaker by using the time recurrent neural network. a corresponding voice segment to establish a voiceprint model, and a preset type vector for characterizing the speaker identity feature is extracted based on the voiceprint model; the calculating unit 1022 is configured to calculate a voice corresponding to each voice frame based on the preset type vector a maximum posterior probability of the person; a first adjusting unit 1023, configured to adjust the mixed Gaussian model of the speaker based on the maximum posterior probability and using a predetermined algorithm; and a second adjusting unit 1024, configured to adjust the mixed Gaussian model Obtaining a speaker with the highest probability corresponding to each voice frame, and adjusting a corresponding segmentation boundary in the mixed voice according to a probability relationship between the speaker with the highest probability and the voice frame; the iteration unit 1025 is configured to update the sound in an iterative manner The pattern is n times, and the mixed Gaussian model is superimposed m times each time the voiceprint model is updated to obtain an effective speech segment corresponding to each speaker, and both n and m are greater than 1 Number.

In this embodiment, a temporal recursive neural network is used to establish a voiceprint model for a phrase segment corresponding to each speaker identifier, and a preset type vector that characterizes the speaker identity feature is extracted based on the voiceprint model. Preferably, the pre-preparation Let the type vector be an i-vector vector, which is an important feature that reflects the speaker's acoustic difference.

In the whole mixed speech, the maximum posterior probability of each speaker is calculated according to the preset type vector, and the mixed Gaussian model of the speaker is re-adjusted by the preset algorithm in the mixed speech by calculating the maximum posterior probability. For example, the speaker's mixed Gaussian model is re-adjusted by the Baum-Welch algorithm, which is a set of k (typically 3-5) Gaussian models. Use the re-adjusted mixed Gaussian model to find the speaker with the highest probability of each speech frame. The segmentation boundary of the mixed speech is adjusted according to the probability relationship between the speech frame and the found speaker, for example, the segmentation boundary is fine-tuned forward or fine-tuned backward. Finally, the above-mentioned voiceprint model is updated n times, and each time the voiceprint model is updated, the m-mixed Gaussian model is superimposed to obtain an effective speech segment corresponding to each speaker, and n and m are positive integers greater than 1.

In this embodiment, a voiceprint model is established by means of a deep learning time recurrent neural network, and each voice frame corresponding to each speaker voice pattern is used to calculate a probability that the voice frame belongs to a certain speaker, and the probability correction model is finally adjusted. The boundary of speech segmentation can effectively improve the accuracy of speaker speech segmentation, reduce the error rate, and have good scalability.

In a preferred embodiment, the apparatus for voice segmentation further includes: a feedback module, configured to acquire a corresponding response content based on the valid voice segment, and the response is Content is fed back to the terminal.

In this embodiment, the automatic response system is associated with a corresponding response library, and the response library stores response content corresponding to different questions. After receiving the mixed voice sent by the terminal, the automatic response system divides the response into a speaker identifier. An effective voice segment, from which an effective voice segment related to the automatic answering system is obtained, and the valid voice segment is matched in the response library, and the matched response content is fed back to the terminal.

The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalents, improvements, etc., which are within the spirit and scope of the present invention, should be included in the protection of the present invention. Within the scope.

Claims (6)

A method for voice segmentation, comprising: S1, when receiving the mixed voice sent by the terminal, the automatic answering system divides the mixed voice into a plurality of phrase segments, and marks a corresponding speaker identifier for each phrase segment; The step S1 includes: S11, acquiring a silent segment in the mixed voice, and removing a silent segment in the mixed voice, to segment the mixed voice according to the silent segment, to obtain a long segment after segmentation. S12, the long speech segment is segmented to extract acoustic features of each long speech segment; S13, KL distance analysis is performed on the acoustic features of each long speech segment, and the long speech is performed according to the KL distance analysis result. The segment is segmented to obtain a segmented phrase segment, wherein the KL distance is a relative entropy; and S14, a Gaussian mixture model is used to perform speech clustering on each phrase segment, and a phrase segment of the same phonetic class Marking a corresponding speaker identifier; S2, using a time recurrent neural network to establish a voiceprint model for a phrase segment corresponding to each speaker identifier, and adjusting the mixed voice based on the voiceprint model Corresponding segmentation boundary to segment the valid speech segment corresponding to each speaker identifier; wherein the step S2 includes: S21, using the time recurrent neural network to establish a voiceprint model for the phrase segment corresponding to each speaker identifier, Extracting a preset type vector representing the speaker identity feature based on the voiceprint model; S22, calculating a maximum posterior probability of the speaker corresponding to each voice frame genre based on the preset type vector; S23, based on the maximum Check the rate and adjust the speaker's mix using a predetermined algorithm a Gaussian model; S24, obtaining a speaker with the highest probability corresponding to each voice frame based on the adjusted mixed Gaussian model, and adjusting a corresponding segmentation boundary in the mixed voice according to a probability relationship between the speaker with the highest probability and the voice frame; And S25, updating the voiceprint model n times in an iterative manner, and superimposing the mixed Gaussian model m times each time the voiceprint model is updated, to obtain valid voice segments corresponding to each speaker, where n and m are greater than A positive integer of 1. The method for voice segmentation according to claim 1, wherein the step S13 comprises: performing KL distance analysis on the acoustic features of each long segment of speech, and the KL distance for the long segment of the speech whose duration is greater than the preset time threshold. The maximum value is scored to obtain a segmented phrase segment. The method for voice segmentation according to claim 1, wherein the step S2 further comprises: acquiring a corresponding response content based on the valid voice segment, and feeding back the response content to the terminal. A device for voice segmentation, comprising: a segmentation module, configured to divide the mixed speech into a plurality of phrase segments and receive a corresponding speaker identifier for each phrase segment when receiving the mixed speech sent by the terminal; The segmentation module includes: a removal unit, configured to acquire a silence segment in the mixed voice, and remove a silence segment in the mixed voice, to segment the mixed voice according to the silence segment, and obtain a segmentation a long speech segment; a framing unit for framing the long speech segment to extract an acoustic feature of each long speech segment; a segmentation unit configured to perform KL distance analysis on the acoustic features of each long segment of speech, and segment the long segment of speech according to the KL distance analysis result to obtain a segmented phrase segment, wherein the KL distance is a relative entropy; and a clustering unit for performing voice clustering on each phrase segment by using a Gaussian mixture model, and marking a corresponding speaker identifier for a phrase segment of the same phonetic class; and adjusting a module for utilizing time recursion The neural network establishes a voiceprint model for the phrase segments corresponding to the speaker identifiers, and adjusts corresponding segmentation boundaries in the mixed voice based on the voiceprint model to segment the valid voice segments corresponding to the speaker identifiers; The adjustment module includes: a modeling unit, configured to establish a voiceprint model for the phrase segments corresponding to each speaker identifier by using the time recurrent neural network, and extract a preset representing the speaker identity feature based on the voiceprint model a calculation unit, configured to calculate a maximum posterior probability of a speaker corresponding to each voice frame genre based on the preset type vector; a first adjustment unit, configured to Describe the maximum posterior probability and adjust the mixed Gaussian model of the speaker by using a predetermined algorithm; the second adjusting unit is configured to acquire the speaker with the highest probability corresponding to each voice frame based on the adjusted mixed Gaussian model, and according to the maximum probability a probability relationship between the speaker and the speech frame adjusts a corresponding segmentation boundary in the mixed speech; and an iteration unit, configured to update the voiceprint model n times in an iterative manner, and iteratively m times each time the voiceprint model is updated The Gaussian model is mixed to obtain valid speech segments corresponding to each speaker, and n and m are positive integers greater than one. The apparatus for voice segmentation according to Item 4, wherein the segmentation unit is specifically configured to perform KL distance analysis on acoustic characteristics of each long segment of speech, and the duration is greater than a preset. The long speech segment of the time threshold is segmented at the maximum of the KL distance to obtain a segmented phrase segment. The device for voice segmentation according to claim 4, wherein the device for voice segmentation further comprises: a feedback module, configured to acquire a corresponding response content based on the valid voice segment, and feed back the response content to The terminal.