Deep Learning Techniques in Tandem with Signal Processing Cues for Phonetic Segmentation for Text to Speech Synthesis in Indian Languages

Abstract

Automatic detection of phoneme boundaries is an important sub-task in building speech processing applications, especially text-to-speech synthesis (TTS) systems. The main drawback of the Gaussian mixture model-hidden Markov model (GMM-HMM) based forced-alignment is that the phoneme boundaries are not explicitly modeled. In an earlier work, we had proposed the use of signal processing cues in tandem with GMM-HMM based forced alignment for boundary correction for building Indian language TTS systems. In this paper, we capitalise on the ability of robust acoustic modeling techniques such as deep neural networks (DNN) and convolutional deep neural networks (CNN) for acoustic modeling. The GMM-HMM based forced alignment is replaced by DNN-HMM/CNN-HMM based forced alignment. Signal processing cues are used to correct the segment boundaries obtained using DNN-HMM/CNN-HMM segmentation. TTS systems built using these boundaries show a relative improvement in synthesis quality.

Audio Segmentation Techniques and Applications Based on Deep Learning

“…In Figure 3, we represent the research work related to audio segmentation that started majorly in 2005. Until a decade, there was a very slow increase in this type of research, but post-2016, there was a sharp rise in this area of research [17]. e exponential increase in research trends can be seen in audio segmentation-related research since 2017.…”

Code-switching in Indic Speech Synthesisers

“…With syllable and phone labels, phone alignments of speech wavefiles are obtained using signal processing cues in tandem with deep learning techniques [13]. First, syllable level boundaries are determined with the help of spectral cues [26].…”