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DIA-TTS: Deep-Inherited Attention-Based Text-to-Speech Synthesizer.

Junxiao Yu1, Zhengyuan Xu1,2, Xu He1

  • 1Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China.

Entropy (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

This study introduces a novel text-to-speech (TTS) model using deep-inherited attention (DIA) to improve naturalness and accuracy in synthesized speech, especially for long sentences. The DIA mechanism enhances phrase break prediction and attention robustness for more human-like audio.

Keywords:
deep learningdeep neural networkinformation theorylocal-sensitive attentionnatural language processingtext-to-speech

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Area of Science:

  • Speech Synthesis
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional sequence-to-sequence (seq2seq) text-to-speech (TTS) models like Tacotron2 struggle with long sentences due to single soft attention, leading to unnatural speech with incorrect word generation and poor prosody.
  • Existing TTS systems often fail to capture emotional nuances and natural speech rhythm, limiting their effectiveness as assistive tools.

Purpose of the Study:

  • To propose an end-to-end neural generative TTS model that overcomes the limitations of traditional seq2seq models.
  • To enhance the naturalness, accuracy, and prosodic quality of synthesized speech, particularly concerning phrase breaks and emotional expression.

Main Methods:

  • Developed a novel TTS model incorporating a deep-inherited attention (DIA) mechanism with an adjustable local-sensitive factor (LSF).
  • Utilized a multi-RNN block in the decoder for improved acoustic feature extraction and employed hidden-state information for attention alignment.
  • Integrated the DIA mechanism with a multi-RNN decoder and employed WaveGlow as a vocoder for real-time audio synthesis.

Main Results:

  • The proposed DIA-TTS model demonstrated superior performance in predicting phrase breaks, leading to more natural-sounding synthesized speech.
  • Human subjective experiments yielded a high Mean Opinion Score (MOS) of 4.48 for naturalness.
  • Ablation studies confirmed the effectiveness of the DIA mechanism in enhancing phrase break prediction and overall attention robustness.

Conclusions:

  • The DIA mechanism, combined with multi-RNN layers and LSF, significantly improves the quality and naturalness of synthesized speech compared to traditional methods.
  • The proposed model offers a robust solution for generating human-like speech with accurate prosody and emotional expression.
  • This advancement holds promise for developing more effective assistive tools and enhancing human-computer interaction through speech synthesis.