SC-GlowTTS: An Efficient Zero-Shot Multi-Speaker Text-To-Speech Model (original) (raw)
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IEEE Access, 2018
WaveNet, which learns directly from speech waveform samples, has been used as an alternative to vocoders and achieved very high-quality synthetic speech in terms of both naturalness and speaker similarity even in multi-speaker text-to-speech synthesis systems. However, the WaveNet vocoder uses acoustic features as local condition parameters, and these parameters need to be accurately predicted by another acoustic model. So far, it is not yet clear how to train this acoustic model, which is problematic because the final quality of synthetic speech is significantly affected by the performance of the acoustic model. Significant degradation occurs, especially when predicted acoustic features have mismatched characteristics compared to natural ones. In order to reduce the mismatched characteristics between natural and generated acoustic features, we propose new frameworks that incorporate either a conditional generative adversarial network (GAN) or its variant, Wasserstein GAN with gradient penalty (WGAN-GP), into multi-speaker speech synthesis that uses the WaveNet vocoder. The GAN generator performs as an acoustic model and its outputs are used as the local condition parameters of the WaveNet. We also extend the GAN frameworks and use the discretized-mixture-of-logistics (DML) loss of a well-trained WaveNet in addition to mean squared error and adversarial losses as parts of objective functions. Experimental results show that acoustic models trained using the WGAN-GP framework using back-propagated DML loss achieves the highest subjective evaluation scores in terms of both quality and speaker similarity. INDEX TERMS Generative adversarial network, multi-speaker modeling, speech synthesis, WaveNet.
Normalization Driven Zero-Shot Multi-Speaker Speech Synthesis
Interspeech 2021, 2021
In this paper, we present a novel zero-shot multi-speaker speech synthesis approach (ZSM-SS) that leverages the normalization architecture and speaker encoder with non-autoregressive multi-head attention driven encoder-decoder architecture.Given an input text and a reference speech sample of an unseen person, ZSM-SS can generate speech in that person's style in a zero-shot manner. Additionally, we demonstrate how the affine parameters of normalization help in capturing the prosodic features such as energy and fundamental frequency in a disentangled fashion and can be used to generate morphed speech output. We demonstrate the efficacy of our proposed architecture on multi-speaker VCTK[1] and LibriTTS [2] datasets, using multiple quantitative metrics that measure generated speech distortion and MOS, along with speaker embedding analysis of the proposed speaker encoder model.
Multi-speaker TTS with Deep Learning
2020
Recent advancements in technology have allowed for great development in the field of Speech Synthesis. As such, present-day speech synthesis applications are expected to function for multiple voices, and ensure a fast generation of natural–sounding synthetic speech for enhanced feasibility. This study suggests a multi-speaker text-to-speech (TTS) system for European Portuguese that enables the addition of new speakers without requiring extensive training and data. The proposed model framework comprises two systems: a sequence-to-sequence (Seq2Seq) regressive stage for acoustic feature prediction, followed by a neural vocoder for waveform generation. The model employs a universal vocoder which does not require fine-tuning for new voices. The Seq2Seq regressive model predicts acoustic features in the form of Mel-spectrograms by decoding the combination of linguistic embeddings — extracted from the text input —, and speaker embeddings conveying the target speaker identity. The model op...
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
State-of-the-art text-to-speech (TTS) systems require several hours of recorded speech data to generate high-quality synthetic speech. When using reduced amounts of training data, standard TTS models suffer from speech quality and intelligibility degradations, making training low-resource TTS systems problematic. In this paper, we propose a novel extremely low-resource TTS method called Voice Filter that uses as little as one minute of speech from a target speaker. It uses voice conversion (VC) as a post-processing module appended to a pre-existing high-quality TTS system and marks a conceptual shift in the existing TTS paradigm, framing the few-shot TTS problem as a VC task. Furthermore, we propose to use a duration-controllable TTS system to create a parallel speech corpus to facilitate the VC task. Results show that the Voice Filter outperforms state-of-the-art few-shot speech synthesis techniques in terms of objective and subjective metrics on one minute of speech on a diverse set of voices, while being competitive against a TTS model built on 30 times more data. 1
SVSNet: An End-to-End Speaker Voice Similarity Assessment Model
IEEE Signal Processing Letters, 2022
Neural evaluation metrics derived for numerous speech generation tasks have recently attracted great attention. In this paper, we propose SVSNet, the first end-to-end neural network model to assess the speaker voice similarity between converted speech and natural speech for voice conversion tasks. Unlike most neural evaluation metrics that use hand-crafted features, SVSNet directly takes the raw waveform as input to more completely utilize speech information for prediction. SVSNet consists of encoder, co-attention, distance calculation, and prediction modules and is trained in an end-to-end manner. The experimental results on the Voice Conversion Challenge 2018 and 2020 (VCC2018 and VCC2020) datasets show that SVSNet outperforms well-known baseline systems in the assessment of speaker similarity at the utterance and system levels.
Improving multi-speaker TTS prosody variance with a residual encoder and normalizing flows
ArXiv, 2021
Text-to-speech systems recently achieved almost indistinguishable quality from human speech. However, the prosody of those systems is generally flatter than natural speech, producing samples with low expressiveness. Disentanglement of speaker id and prosody is crucial in text-to-speech systems to improve on naturalness and produce more variable syntheses. This paper proposes a new neural text-to-speech model that approaches the disentanglement problem by conditioning a Tacotron2-like architecture on flow-normalized speaker embeddings, and by substituting the reference encoder with a new learned latent distribution responsible for modeling the intra-sentence variability due to the prosody. By removing the reference encoder dependency, the speaker-leakage problem typically happening in this kind of systems disappears, producing more distinctive syntheses at inference time. The new model achieves significantly higher prosody variance than the baseline in a set of quantitative prosody f...
Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning
ArXiv, 2021
Deep learning models are becoming predominant in many fields of machine learning. Text-to-Speech (TTS), the process of synthesizing artificial speech from text, is no exception. To this end, a deep neural network is usually trained using a corpus of several hours of recorded speech from a single speaker. Trying to produce the voice of a speaker other than the one learned is expensive and requires large effort since it is necessary to record a new dataset and retrain the model. This is the main reason why the TTS models are usually single speaker. The proposed approach has the goal to overcome these limitations trying to obtain a system which is able to model a multi-speaker acoustic space. This allows the generation of speech audio similar to the voice of different target speakers, even if they were not observed during the training phase.
Many-to-Many Voice Conversion with Out-of-Dataset Speaker Support
ArXiv, 2019
We present a Cycle-GAN based many-to-many voice conversion method that can convert between speakers that are not in the training set. This property is enabled through speaker embeddings generated by a neural network that is jointly trained with the Cycle-GAN. In contrast to prior work in this domain, our method enables conversion between an out-of-dataset speaker and a target speaker in either direction and does not require re-training. Out-of-dataset speaker conversion quality is evaluated using an independently trained speaker identification model, and shows good style conversion characteristics for previously unheard speakers. Subjective tests on human listeners show style conversion quality for in-dataset speakers is comparable to the state-of-the-art baseline model.
Voice Conversion Across Arbitrary Speakers Based on a Single Target-Speaker Utterance
Interspeech 2018
Developing a voice conversion (VC) system for a particular speaker typically requires considerable data from both the source and target speakers. This paper aims to effectuate VC across arbitrary speakers, which we call any-to-any VC, with only a single target-speaker utterance. Two systems are studied: (1) the i-vector-based VC (IVC) system and (2) the speakerencoder-based VC (SEVC) system. Phonetic PosteriorGrams are adopted as speaker-independent linguistic features extracted from speech samples. Both systems train a multi-speaker deep bidirectional long-short term memory (DBLSTM) VC model, taking in additional inputs that encode speaker identities, in order to generate the outputs. In the IVC system, the speaker identity of a new target speaker is represented by i-vectors. In the SEVC system, the speaker identity is represented by speaker embedding predicted from a separately trained model. Experiments verify the effectiveness of both systems in achieving VC based only on a single target-speaker utterance. Furthermore, the IVC approach is superior to SEVC, in terms of the quality of the converted speech and its similarity to the utterance produced by the genuine target speaker.
arXiv (Cornell University), 2023
In this paper, we present a Diffusion GAN based approach (Prosodic Diff-TTS) to generate the corresponding high-fidelity speech based on the style description and content text as an input to generate speech samples within only 4 denoising steps. It leverages the novel conditional prosodic layer normalization to incorporate the style embeddings into the multi head attention based phoneme encoder and mel spectrogram decoder based generator architecture to generate the speech. The style embedding is generated by fine tuning the pretrained BERT model on auxiliary tasks such as pitch, speaking speed, emotion,gender classifications. We demonstrate the efficacy of our proposed architecture on multi-speaker LibriTTS and PromptSpeech datasets, using multiple quantitative metrics that measure generated accuracy and MOS.