Integration of Spectral Feature Extraction and Modeling for HMM-Based Speech Synthesis

By Kazuhiro Nakamura, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda. 2014

5 citations recorded by the source corpus.

This paper proposes a novel approach for integrating spectral feature extraction and acoustic modeling in hidden Markov model (HMM) based speech synthesis. The statistical modeling process of speech waveforms is typically divided into two component modules: the frame-byframe feature extraction module and the acoustic modeling module. In the feature extraction module, the statistical mel-cepstral analysis technique has been used and the objective function is the likelihood of mel-cepstral coefficients for given speech waveforms. In the acoustic modeling module, the objective function is the likelihood of model parameters for given melcepstral coefficients. It is important to improve the performance of each component module for achieving higher quality synthesized speech. However, the final objective of speech synthesis systems is to generate natural speech waveforms from given texts, and the improvement of each component module does not always lead to the improvement of the quality of synthesized speech. Therefore, ideally all objective functions should be optimized based on an integrated criterion which well represents subjective speech quality of human perception. In this paper, we propose an approach to model speech waveforms directly and optimize the final objective function. Experimental results show that the proposed method outperformed the conventional methods in objective and subjective measures. key words: integrative model, HMM-based speech synthesis, acoustic modeling, mel-cepstral analysis, trajectory HMM

Subfields: Artificial Intelligence, Natural Language Processing, Speech & Audio, Signal Processing

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Citations in the corpus

WaveNet: A Generative Model for Raw Audio (2016, background): “There have been attempts to integrate these two steps into a single one (Toda & Tokuda, 2008; Wu & Tokuda, 2008; Maia et al., 2010; Nakamura et al., 2014; Muthukumar & Black, 2014; Tokuda & Zen, 2015; 2016; Takaki & Yamagishi, 2016).”
Directly modeling speech waveforms by neural networks for statistical parametric speech synthesis (2015, methodology): “…and acoustic model training into a single framework, e.g. the log spectral distortion-version of minimum generation error training (MGE LSD) [11], statistical vocoder (STAV OCO) [12], waveform-level statistical model [13], and mel-cepstral analysis-integrated hidden Markov models (HMMs) [14].”
Directly modeling voiced and unvoiced components in speech waveforms by neural networks (2016, background): “There have been a couple of attempts to integrate acoustic feature extraction into acoustic modeling, such as the log spectral distortion-version of minimum generation error training [7], statistical vocoder [8], waveform-level statistical model [9], and melcepstral analysis-integrated hidden Markov”