Zero-Shot vs. Few-Shot Multi-Speaker TTS Using Pre-trained Czech SpeechT5 Model

• URL: http://arxiv.org/abs/2407.17167v1
• Date: Wed, 24 Jul 2024 11:14:06 GMT
• Title: Zero-Shot vs. Few-Shot Multi-Speaker TTS Using Pre-trained Czech SpeechT5 Model
• Authors: Jan Lehečka, Zdeněk Hanzlíček, Jindřich Matoušek, Daniel Tihelka,
• Abstract summary: We show that SpeechT5 model can generate a synthetic voice for any speaker using only one minute of the target speaker's data. We successfully demonstrated the high quality and similarity of our synthetic voices on publicly known Czech politicians and celebrities.
• Score: 3.462371782084948
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we experimented with the SpeechT5 model pre-trained on large-scale datasets. We pre-trained the foundation model from scratch and fine-tuned it on a large-scale robust multi-speaker text-to-speech (TTS) task. We tested the model capabilities in a zero- and few-shot scenario. Based on two listening tests, we evaluated the synthetic audio quality and the similarity of how synthetic voices resemble real voices. Our results showed that the SpeechT5 model can generate a synthetic voice for any speaker using only one minute of the target speaker's data. We successfully demonstrated the high quality and similarity of our synthetic voices on publicly known Czech politicians and celebrities.

Related papers

• Pheme: Efficient and Conversational Speech Generation [52.34331755341856]
  We introduce the Pheme model series that offers compact yet high-performing conversational TTS models. It can be trained efficiently on smaller-scale conversational data, cutting data demands by more than 10x but still matching the quality of the autoregressive TTS models.
  arXiv  Detail & Related papers  (2024-01-05T14:47:20Z)
• Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer Learning [3.5032870024762386]
  This paper presents a novel approach that leverages the Fastpitch text-to-speech (TTS) model for generating high-quality synthetic child speech. The approach involved finetuning a multi-speaker TTS model to work with child speech. We conducted an objective assessment that showed a significant correlation between real and synthetic child voices.
  arXiv  Detail & Related papers  (2023-11-07T19:31:44Z)
• NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers [90.83782600932567]
  We develop NaturalSpeech 2, a TTS system that leverages a neural audio predictor with residual vectorizers to get the quantized latent vectors. We scale NaturalSpeech 2 to large-scale datasets with 44K hours of speech and singing data and evaluate its voice quality on unseen speakers. NaturalSpeech 2 outperforms previous TTS systems by a large margin in terms of prosody/timbre similarity, synthesis, and voice quality in a zero-shot setting.
  arXiv  Detail & Related papers  (2023-04-18T16:31:59Z)
• Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers [92.55131711064935]
  We introduce a language modeling approach for text to speech synthesis (TTS) Specifically, we train a neural language model (called Vall-E) using discrete codes derived from an off-the-shelf neural audio model. Vall-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech.
  arXiv  Detail & Related papers  (2023-01-05T15:37:15Z)
• AdaSpeech 4: Adaptive Text to Speech in Zero-Shot Scenarios [143.47967241972995]
  We develop AdaSpeech 4, a zero-shot adaptive TTS system for high-quality speech synthesis. We model the speaker characteristics systematically to improve the generalization on new speakers. Without any fine-tuning, AdaSpeech 4 achieves better voice quality and similarity than baselines in multiple datasets.
  arXiv  Detail & Related papers  (2022-04-01T13:47:44Z)
• GANSpeech: Adversarial Training for High-Fidelity Multi-Speaker Speech Synthesis [6.632254395574993]
  GANSpeech is a high-fidelity multi-speaker TTS model that adopts the adversarial training method to a non-autoregressive multi-speaker TTS model. In the subjective listening tests, GANSpeech significantly outperformed the baseline multi-speaker FastSpeech and FastSpeech2 models.
  arXiv  Detail & Related papers  (2021-06-29T08:15:30Z)
• Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning [0.802904964931021]
  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.
  arXiv  Detail & Related papers  (2021-02-10T18:43:56Z)
• Noise Robust TTS for Low Resource Speakers using Pre-trained Model and Speech Enhancement [31.33429812278942]
  The proposed end-to-end speech synthesis model uses both speaker embedding and noise representation as conditional inputs to model speaker and noise information respectively. Experimental results show that the speech generated by the proposed approach has better subjective evaluation results than the method directly fine-tuning multi-speaker speech synthesis model.
  arXiv  Detail & Related papers  (2020-05-26T06:14:06Z)
• Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation [51.37980448183019]
  We propose Audio ALBERT, a lite version of the self-supervised speech representation model. We show that Audio ALBERT is capable of achieving competitive performance with those huge models in the downstream tasks. In probing experiments, we find that the latent representations encode richer information of both phoneme and speaker than that of the last layer.
  arXiv  Detail & Related papers  (2020-05-18T10:42:44Z)
• Semi-supervised Learning for Multi-speaker Text-to-speech Synthesis Using Discrete Speech Representation [125.59372403631006]
  We propose a semi-supervised learning approach for multi-speaker text-to-speech (TTS) A multi-speaker TTS model can learn from the untranscribed audio via the proposed encoder-decoder framework with discrete speech representation. We found the model can benefit from the proposed semi-supervised learning approach even when part of the unpaired speech data is noisy.
  arXiv  Detail & Related papers  (2020-05-16T15:47:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.