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Most Indians are inherently bilingual or multilingual owing to the diverse linguistic culture in India. As a result, code-switching is quite common in conversational speech. The objective of this work is to train good quality text-to-speech (TTS) synthesisers that can seamlessly handle code-switching. To achieve this, bilingual TTSes that are capable of handling phonotactic variations across languages are trained using combinations of monolingual data in a unified framework. In addition to segmenting Indic speech data using signal processing cues in tandem with hidden Markov model-deep neural network (HMM-DNN), we propose to segment Indian English data using the same approach after NIST syllabification. Then, bilingual HTS-STRAIGHT based systems are trained by randomizing the order of data so that the systematic interactions between the two languages are captured better. Experiments are conducted by considering three language pairs: Hindi+English, Tamil+English and Hindi+Tamil. The code-switched systems are evaluated on monolingual, code-mixed and code-switched texts. Degradation mean opinion score (DMOS) for monolingual sentences shows marginal degradation over that of an equivalent monolingual TTS system, while the DMOS for bilingual sentences is significantly better than that of the corresponding monolingual TTS systems.

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.

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