Spontaneous Style Text-to-Speech Synthesis with Controllable Spontaneous Behaviors Based on Language Models

• URL: http://arxiv.org/abs/2407.13509v1
• Date: Thu, 18 Jul 2024 13:42:38 GMT
• Title: Spontaneous Style Text-to-Speech Synthesis with Controllable Spontaneous Behaviors Based on Language Models
• Authors: Weiqin Li, Peiji Yang, Yicheng Zhong, Yixuan Zhou, Zhisheng Wang, Zhiyong Wu, Xixin Wu, Helen Meng,
• Abstract summary: We propose a novel spontaneous speech synthesis system based on language models. Fine-grained prosody modeling is introduced to enhance the model's ability to capture subtle prosody variations in spontaneous speech.
• Score: 55.898594710420326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spontaneous style speech synthesis, which aims to generate human-like speech, often encounters challenges due to the scarcity of high-quality data and limitations in model capabilities. Recent language model-based TTS systems can be trained on large, diverse, and low-quality speech datasets, resulting in highly natural synthesized speech. However, they are limited by the difficulty of simulating various spontaneous behaviors and capturing prosody variations in spontaneous speech. In this paper, we propose a novel spontaneous speech synthesis system based on language models. We systematically categorize and uniformly model diverse spontaneous behaviors. Moreover, fine-grained prosody modeling is introduced to enhance the model's ability to capture subtle prosody variations in spontaneous speech.Experimental results show that our proposed method significantly outperforms the baseline methods in terms of prosody naturalness and spontaneous behavior naturalness.

Related papers

• NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models [127.47252277138708]
  We propose NaturalSpeech 3, a TTS system with factorized diffusion models to generate natural speech in a zero-shot way. Specifically, we design a neural with factorized vector quantization (FVQ) to disentangle speech waveform into subspaces of content, prosody, timbre, and acoustic details. Experiments show that NaturalSpeech 3 outperforms the state-of-the-art TTS systems on quality, similarity, prosody, and intelligibility.
  arXiv  Detail & Related papers  (2024-03-05T16:35:25Z)
• Towards Spontaneous Style Modeling with Semi-supervised Pre-training for Conversational Text-to-Speech Synthesis [53.511443791260206]
  We propose a semi-supervised pre-training method to increase the amount of spontaneous-style speech and spontaneous behavioral labels. In the process of semi-supervised learning, both text and speech information are considered for detecting spontaneous behaviors labels in speech.
  arXiv  Detail & Related papers  (2023-08-31T09:50:33Z)
• Prosody-controllable spontaneous TTS with neural HMMs [11.472325158964646]
  We propose a TTS architecture that can rapidly learn to speak from small and irregular datasets. We add utterance-level prosody control to an existing neural HMM-based TTS system. We evaluate the system's capability of synthesizing two types of creaky voice.
  arXiv  Detail & Related papers  (2022-11-24T11:06:11Z)
• StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech Synthesis [23.17929822987861]
  StyleTTS is a style-based generative model for parallel TTS that can synthesize diverse speech with natural prosody from a reference speech utterance. Our method significantly outperforms state-of-the-art models on both single and multi-speaker datasets.
  arXiv  Detail & Related papers  (2022-05-30T21:34:40Z)
• Hierarchical prosody modeling and control in non-autoregressive parallel neural TTS [7.531331499935223]
  We train a non-autoregressive parallel neural TTS model hierarchically conditioned on coarse and fine-grained acoustic speech features. Experiments show that a non-autoregressive TTS model hierarchically conditioned on utterance-wise pitch, pitch range, duration, energy, and spectral tilt can effectively control each prosodic dimension.
  arXiv  Detail & Related papers  (2021-10-06T17:58:42Z)
• Ctrl-P: Temporal Control of Prosodic Variation for Speech Synthesis [68.76620947298595]
  Text does not fully specify the spoken form, so text-to-speech models must be able to learn from speech data that vary in ways not explained by the corresponding text. We propose a model that generates speech explicitly conditioned on the three primary acoustic correlates of prosody.
  arXiv  Detail & Related papers  (2021-06-15T18:03:48Z)
• Towards Multi-Scale Style Control for Expressive Speech Synthesis [60.08928435252417]
  The proposed method employs a multi-scale reference encoder to extract both the global-scale utterance-level and the local-scale quasi-phoneme-level style features of the target speech. During training time, the multi-scale style model could be jointly trained with the speech synthesis model in an end-to-end fashion.
  arXiv  Detail & Related papers  (2021-04-08T05:50:09Z)
• Hierarchical Prosody Modeling for Non-Autoregressive Speech Synthesis [76.39883780990489]
  We analyze the behavior of non-autoregressive TTS models under different prosody-modeling settings. We propose a hierarchical architecture, in which the prediction of phoneme-level prosody features are conditioned on the word-level prosody features.
  arXiv  Detail & Related papers  (2020-11-12T16:16:41Z)
• Prosody Learning Mechanism for Speech Synthesis System Without Text Length Limit [39.258370942013165]
  A prosody learning mechanism is proposed to model the prosody of speech based on TTS system. A novel self-attention structure, named as local attention, is proposed to lift this restriction of input text length. Experiments on English and Mandarin show that speech with more satisfactory prosody has obtained in our model.
  arXiv  Detail & Related papers  (2020-08-13T02:54:50Z)

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.