IP Library › Patent Application 18962686

Patent Application

App. No. 18/962,686

USING NON-PARALLEL VOICE CONVERSION FOR SPEECH CONVERSION MODELS

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Patent No.: US None
App. No.: 18/962,686

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Abstract

A method includes receiving a set of training utterances each including a non-synthetic speech representation of a corresponding utterance, and for each training utterance, generating a corresponding synthetic speech representation by using a voice conversion model. The non-synthetic speech representation and the synthetic speech representation form a corresponding training utterance pair. At each of a plurality of output steps for each training utterance pair, the method also includes generating, for output by a speech recognition model, a first probability distribution over possible non-synthetic speech recognition hypotheses for the non-synthetic speech representation and a second probability distribution over possible synthetic speech recognition hypotheses for the synthetic speech representation. The method also includes determining a consistent loss term for the corresponding training utterance pair based on the first and second probability distributions and updating parameters of the speech recognition model based on the consistent loss term.

Claims (64)

1 . A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations comprising:

obtaining a pretrained automatic speech recognition (ASR) encoder previously trained on ASR loss for a speech recognition task;

receiving a training utterance comprising a non-synthetic speech representation of a corresponding utterance;

generating, using the pretrained ASR encoder, a latent representation of the non-synthetic speech representation of the corresponding utterance;

generating a corresponding voice conversion synthetic speech representation by using a voice conversion model to convert the non-synthetic speech representation into the corresponding voice conversion synthetic speech representation of the corresponding utterance, wherein the non-synthetic speech representation and the voice conversion synthetic speech representation form a corresponding training utterance pair;

based on the latent representation of the non-synthetic speech representation and the voice conversion synthetic speech representation of the corresponding utterance, generating a voice conversion model loss; and

training the voice conversion model on the voice conversion model loss while parameters of the pretrained ASR encoder are frozen.

2 . The method of claim 1 , wherein the operations further comprise, after training the voice conversion model on the voice conversion model loss, at each of a plurality of output steps:

generating, for output by a speech recognition model comprising the pretrained ASR encoder and a speech decoder, a first probability distribution over possible non-synthetic speech recognition hypotheses for the non-synthetic speech representation of the corresponding utterance;

generating, for output by the speech recognition model, a second probability distribution over possible synthetic speech recognition hypotheses for the corresponding synthetic speech representation of the corresponding utterance;

generating a non-synthetic speech loss term based on the first probability distribution over possible non-synthetic speech recognition hypotheses for the non-synthetic speech representation of the corresponding utterance and a ground-truth transcription of the corresponding utterance; and

generating a synthetic speech loss term based on the second probability distribution over possible synthetic speech recognition hypotheses for the corresponding synthetic speech representation of the corresponding utterance and the ground-truth transcription of the corresponding utterance.

3 . The method of claim 2 , wherein the operations further comprise updating parameters of the speech recognition model based on the non-synthetic and synthetic speech loss terms generated at each of the plurality of output steps.

4 . The method of claim 1 , wherein:

the non-synthetic speech representation of the corresponding utterance comprises speech spoken by a source speaker and conveys an accent/locale associated with the source speaker; and

the voice conversion synthetic representation of the corresponding utterance comprises synthesized speech representing the corresponding utterance that conveys an accent/local associated with a target speaker.

5 . The method of claim 4 , wherein the accent/local associated with the source speaker is different than the accent/local associated with the target speaker.

6 . The method of claim 4 , wherein the voice conversion synthetic speech representation conveys the same linguistic content as the non-synthetic speech representation of the corresponding utterance.

7 . The method of claim 1 , wherein the voice conversion model comprises a non-parallel voice conversion model.

8 . The method of claim 1 , wherein the voice conversion model comprises:

a content encoder configured to:

receive, as input, input speech;

generate, as output, an initial latent representation;

a vector quantization-variational autoencoder (VQ-VAE) layer configured to:

receive, as input, the initial latent representation; and

generate, as output, a latent representation of linguistic content extracted from the input speech that discards speaking style variations in the input speech; and

a voice conversion decoder conditioned on a speaker embedding and configured to generate output speech based on the latent representation of linguistic content.

9 . The method of claim 8 , wherein the VQ-VAE layer is trained using a VQ loss based on the latent representation of linguistic content generated for each timestep, the VQ loss encouraging the VQ-VAE layer to minimize a distance between an output and a nearest codebook.

10 . The method of claim 8 , wherein the voice conversion decoder is configured to:

receive, as input, the latent representation of linguistic content for the input speech and the speaker embedding; and

generate, as output, the output speech comprising a reconstruction of the input speech.

11 . A system comprising:

data processing hardware; and

memory hardware in communication with the data processing hardware and storing instructions that when executed by the data processing hardware causes the data processing hardware to perform operations comprising:

obtaining a pretrained automatic speech recognition (ASR) encoder previously trained on ASR loss for a speech recognition task;

receiving a training utterance comprising a non-synthetic speech representation of a corresponding utterance;

generating, using the pretrained ASR encoder, a latent representation of the non-synthetic speech representation of the corresponding utterance;

training the voice conversion model on the voice conversion model loss while parameters of the pretrained ASR encoder are frozen.

12 . The system of claim 11 , wherein the operations further comprise, after training the voice conversion model on the voice conversion model loss, at each of a plurality of output steps:

13 . The system of claim 12 , wherein the operations further comprise updating parameters of the speech recognition model based on the non-synthetic and synthetic speech loss terms generated at each of the plurality of output steps.

14 . The system of claim 11 , wherein:

the non-synthetic speech representation of the corresponding utterance comprises speech spoken by a source speaker and conveys an accent/locale associated with the source speaker; and

15 . The system of claim 14 , wherein the accent/local associated with the source speaker is different than the accent/local associated with the target speaker.

16 . The system of claim 14 , wherein the voice conversion synthetic speech representation conveys the same linguistic content as the non-synthetic speech representation of the corresponding utterance.

17 . The system of claim 14 , wherein the voice conversion model comprises a non-parallel voice conversion model.

18 . The system of claim 11 , wherein the voice conversion model comprises: