Ye Jia
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Training Text-To-Speech Systems From Synthetic Data: A Practical Approach For Accent Transfer Tasks
Lev Finkelstein
Norman Casagrande
Alexey Petelin
Jonathan Shen
Yu Zhang
Yonghui Wu
Rob Clark
Interspeech (2022)
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Transfer tasks in text-to-speech (TTS) synthesis — where one
or more aspects of the speech of one set of speakers is transferred
to another set of speakers that do not feature these aspects originally —
remains a challenging task. One of the challenges is that models
that have high-quality transfer capabilities can have issues in stability,
making them impractical for user-facing critical tasks. This paper
demonstrates that transfer can be obtained by training an robust TTS
system on data generated by a less robust TTS system designed for a high-quality
transfer task; In particular, a CHiVE-BERT monolingual TTS
system is trained on the output of a Tacotron model designed
for accent transfer. While some quality loss is inevitable with
this approach, experimental results show that the models trained
on synthetic data this way can produce high quality audio displaying accent
transfer, while preserving speaker characteristics such as speaking style.
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We introduce CVSS, a massively multilingual-to-English speech-to-speech translation (S2ST) corpus, covering sentence-level parallel S2ST pairs from 21 languages into English. CVSS is derived from the Common Voice speech corpus and the CoVoST 2 speech-to-text translation (ST) corpus, by synthesizing the translation text from CoVoST 2 into speech using state-of-the-art TTS systems. Two versions of translation speeches are provided: 1) CVSS-C: All the translation speeches are in a single high-quality canonical voice; 2) CVSS-T: The translation speeches are in voices transferred from the corresponding source speeches. In addition, CVSS provides normalized translation text which matches the pronunciation in the translation speech. On each version of CVSS, we built baseline multilingual direct S2ST models and cascade S2ST models, verifying the effectiveness of the corpus. To build strong cascade S2ST baselines, we trained an ST model on CoVoST 2, which outperforms the previous state-of-the-art trained on the corpus without extra data by 5.8 BLEU. Nevertheless, the performance of the direct S2ST models approaches the strong cascade baselines when trained from scratch, and with only 0.1 or 0.7 BLEU difference on ASR transcribed translation when initialized from matching ST models.
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More than Words: In-the-Wild Visually-Driven Text-to-Speech
Brendan Shillingford
Michael Eyov Hassid
Tal Remez
CVPR, CVF CVPR-2022 (2022)
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In this paper we present VDTTS, a visual-driven TTS model. Unlike most recent text-to-speech methods which are limited by their lack of ability to generate speech with pauses, emotions, prosody and pitch, is able to do so by taking advantage of an additional silent video as an input.Our method is composed of video and text encoders that are combined via a multi-source attention layer. Speech is generated by a mel-spectrogram decoder followed by a vocoder. We evaluate our method on several challenging benchmarks including VoxCeleb2. To the best of our knowledge this is the first time such a method is trained and evaluated on in-the-wild examples that include unseen speakers.Through a rigorous evaluation we demonstrate the superior performance of our method with respect to other recent work both in terms of objective measures as well as human listening studies.
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XTREME-S: Evaluating Cross-lingual Speech Representations
Ankur Bapna
Clara E. Rivera
Mihir Sanjay Kale
Sandy Ritchie
Sebastian Ruder
Simran Khanuja
Yu Zhang
Proc. Interspeech 2022
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We introduce \xtremes, a new benchmark to evaluate universal cross-lingual speech representations in many languages. XTREME-S covers four task families: speech recognition, classification, retrieval and speech-to-text translation. Covering 102 languages from 10+ language families, 3 different domains and 4 task families, XTREME-S aims to simplify multilingual speech representation evaluation, as well as catalyze research in ``universal'' speech representation learning. This paper describes the new benchmark and establishes the first speech-only and speech-text baselines using XLS-R and mSLAM on all downstream tasks. We motivate the design choices and detail how to use the benchmark. The code and pre-processing scripts will be made publicly available.\footnote{\small\url{https://huggingface.co/datasets/google/xtreme_s}}
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We present Translatotron 2, a neural direct speech-to-speech translation model that can be trained end-to-end. Translatotron 2 consists of a speech encoder, a phoneme decoder, a mel-spectrogram synthesizer, and an attention module that connects all the previous three components. Experimental results suggest that Translatotron 2 outperforms the original Translatotron by a large margin in terms of translation quality and predicted speech naturalness, and drastically improves the robustness of the predicted speech.
We also propose a new method for retaining the source speaker's voice in the translated speech. The trained model is restricted to retain the source speaker's voice, but unlike the original Translatotron, it is not able to generate speech in a different speaker's voice, making the model more robust for production deployment, by mitigating potential abuse for ``deepfake'' artifacts. When the new method is used together with a simple concatenation data augmentation, the trained Translatotron 2 model is able to retain each speaker's voice for input with speaker-switching.
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Although neural end-to-end text-to-speech models can synthesizehighly natural speech, there is still a room for improvements in itsefficiency during inference. This paper proposes a non-autoregressiveneural text-to-speech model augmented with a variational autoencoder-based residual encoder. This model, calledParallel Tacotron, is highlyparallelizable during both training and inference, allowing efficientsynthesis on modern parallel hardware. The use of the variationalautoencoder helps to relax the one-to-many mapping nature of thetext-to-speech problem. To further improve the naturalness, weintroduce an iterative spectrogram loss, which is inspired by iterativerefinement, and lightweight convolution, which can efficiently capturelocal contexts. Experimental results show that Parallel Tacotronmatches a strong autoregressive baseline in subjective naturalnesswith significantly decreased inference time.
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In this paper, we propose Textual Echo Cancellation (TEC) --- a framework for cancelling the text-to-speech (TTS) playback signal from overlapped speech. Such a system can largely improve speech recognition performance and user experience for intelligent devices such as smart speakers, as the user can talk to the device while the device is still playing the TTS signal responding to the previous query. We implement this system by using a novel sequence-to-sequence model with multi-source attention that takes both the mixture signal and the source text of the TTS playback as inputs, and predicts the enhanced audio. Experiments show that the textual information of the TTS playback signal is critical to the enhancement performance. Besides, the text sequence is much smaller in size compared with the raw acoustic signal of the TTS playback, and can be immediately transmitted to the device and the ASR server even before the playback signal is synthesized. Therefore, our proposed approach effectively reduces Internet communication and latency compared with alternative approaches such as acoustic echo cancellation (AEC).
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This paper introduces a new encoder model for neural TTS. The proposed model, called PnG BERT, is augmented from the original BERT model, but taking both phoneme and grapheme representation of a text, as well as the word-level alignment between them, as its input. It can be pre-trained on a large text corpus in a self-supervised manner then fine-tuned in a TTS task. The experimental results suggest that PnG BERT can significantly further improve the performance of a state-of-the-art neural TTS model, by producing more appropriate prosody and more accurate pronunciation. Subjective side-by-side preference evaluation showed that raters had no statistically significant preference between the synthesized speech and the ground truth recordings from professional speakers.
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We present a state-of-the-art non-autoregressive Text-To-Speech model. The model called Parallel Tacotron 2 learns to synthesize speech with good quality without supervised duration
signals and other assumptions about the token-to-frame mapping. Specifically, we introduce a novel learned attention mechanism and an iterative reconstruction loss based on Soft Dynamic
Time Warping. We show that this new unsupervised model outperforms the baselines in naturalness in several diverse multi speaker evaluations. Further, we show that the explicit duration model that the model has learned can be used to control the synthesized speech.
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This paper presents Non-Attentive Tacotron based on the Tacotron 2 text-to-speech model, where the attention mechanism is replaced with an explicit duration predictor. This improves robustness significantly as measured by unaligned duration ratio and word deletion rate, two new metrics introduced in this paper for large-scale robustness evaluation using a pre-trained speech recognition model. With the use of Gaussian upsampling, Non-Attentive Tacotron achieves a 5-scale mean opinion score in naturalness of 4.41, slightly outperforming Tacotron 2. The duration predictor enables both utterance-wide and per-phoneme control of duration at inference time. If accurate target duration are scarce or unavailable, it is still possible to train a duration predictor in a semi-supervised or unsupervised manner, with results almost as good as supervised training.
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