Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Abstract
We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech from thousands of speakers without transcripts, to generate a fixed-dimensional embedding vector from seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2, which generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder that converts the mel spectrogram into a sequence of time domain waveform samples. We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the new task, and is able to synthesize natural speech from speakers that were not seen during training. We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance. Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel approach to multispeaker TTS synthesis that effectively utilizes transfer learning to generate speech from unseen speakers using minimal reference audio. The methodology demonstrates significant advancements in speaker representation and synthesis quality, with broad implications for accessibility and speech technology applications.
Methodology
The paper presents a well-structured methodology that effectively decouples the speaker modeling from speech synthesis, allowing for the use of a large, diverse dataset for the speaker encoder while training the TTS model on a smaller dataset. This innovative approach to transfer learning is significant, as it demonstrates the ability to synthesize speech from unseen speakers using only a few seconds of reference audio. The integration of a speaker encoder, a sequence-to-sequence synthesis network, and a WaveNet vocoder is well-justified and shows a clear understanding of the challenges in TTS synthesis.
Results & evaluation
The experiments are robust, utilizing two public datasets (VCTK and LibriSpeech) and employing subjective Mean Opinion Score (MOS) evaluations alongside objective metrics like speaker verification equal error rates (SV-EERs). The results indicate that the proposed model achieves high naturalness and speaker similarity, especially for unseen speakers, which is a critical aspect of the research. However, the paper could benefit from more extensive comparisons with state-of-the-art models to further validate its claims.
Reproducibility
The paper provides sufficient detail on the architecture, training procedures, and datasets used, which should allow for reproducibility by other researchers. However, the proprietary dataset used for training the speaker encoder is a limitation for full reproducibility.
Limitations
Key limitations include the inability to transfer accents and the model's performance being constrained by the small capacity of the speaker embedding. Additionally, the model does not achieve human-level naturalness, which is a significant drawback for practical applications.
Broader impact
The proposed model has the potential to significantly impact accessibility applications, such as aiding individuals who have lost their voice, and could facilitate more natural speech-to-speech translation across languages. However, ethical concerns regarding the potential misuse of voice synthesis technology for impersonation must be addressed. The paper presents a novel approach to multispeaker TTS synthesis that effectively utilizes transfer learning to generate speech from unseen speakers using minimal reference audio. The methodology demonstrates significant advancements in speaker representation and synthesis quality, with broad implications for accessibility and speech technology applications.
Curator note
Jia et al., Google; speaker-conditioned TTS using d-vectors; generalized multi-speaker TTS
Other indexed descriptions from the archive
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis (SV2TTS)
Abstract
We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech from thousands of speakers without transcripts, to generate a fixed-dimensional embedding vector from seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2, which generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder that converts the mel spectrogram into a sequence of time domain waveform samples. We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the new task, and is able to synthesize natural speech from speakers that were not seen during training. We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance. Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation.
AI research summary
The paper presents a novel approach to multispeaker TTS synthesis that effectively utilizes transfer learning to generate speech from unseen speakers using minimal reference audio. The methodology demonstrates significant advancements in speaker representation and synthesis quality, with broad implications for accessibility and speech technology applications.
AI methodology analysis
The paper presents a well-structured methodology that effectively decouples the speaker modeling from speech synthesis, allowing for the use of a large, diverse dataset for the speaker encoder while training the TTS model on a smaller dataset. This innovative approach to transfer learning is significant, as it demonstrates the ability to synthesize speech from unseen speakers using only a few seconds of reference audio. The integration of a speaker encoder, a sequence-to-sequence synthesis network, and a WaveNet vocoder is well-justified and shows a clear understanding of the challenges in TTS synthesis.
AI results analysis
The experiments are robust, utilizing two public datasets (VCTK and LibriSpeech) and employing subjective Mean Opinion Score (MOS) evaluations alongside objective metrics like speaker verification equal error rates (SV-EERs). The results indicate that the proposed model achieves high naturalness and speaker similarity, especially for unseen speakers, which is a critical aspect of the research. However, the paper could benefit from more extensive comparisons with state-of-the-art models to further validate its claims.
AI reproducibility analysis
The paper provides sufficient detail on the architecture, training procedures, and datasets used, which should allow for reproducibility by other researchers. However, the proprietary dataset used for training the speaker encoder is a limitation for full reproducibility.
AI limitations analysis
Key limitations include the inability to transfer accents and the model's performance being constrained by the small capacity of the speaker embedding. Additionally, the model does not achieve human-level naturalness, which is a significant drawback for practical applications.
AI broader impact analysis
The proposed model has the potential to significantly impact accessibility applications, such as aiding individuals who have lost their voice, and could facilitate more natural speech-to-speech translation across languages. However, ethical concerns regarding the potential misuse of voice synthesis technology for impersonation must be addressed. The paper presents a novel approach to multispeaker TTS synthesis that effectively utilizes transfer learning to generate speech from unseen speakers using minimal reference audio. The methodology demonstrates significant advancements in speaker representation and synthesis quality, with broad implications for accessibility and speech technology applications.
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