This paper describes the design and development of Indian language Text-To-Speech (TTS) synthesis systems, using polysyllabic units. Firstly, a phone based TTS is built. Later, a monosyllable cluster unit TTS is built. It is observed that the quality of the synthesized sentences can improve if polysyllable units are used (when the appropriate units are available), since the effects of co-articulation will be preserved in such a case. Hence, we built Hindi and Tamil TTS with polysyllabic units, that contains cluster units of more than one type (monosyllable, bisyllable and trisyllable). The system selects the best set of units during the unit selection process, so as to minimize the join and concatenation costs. Preliminary listening tests indicated that the polysyllable TTS has better quality. ©2010 IEEE.

Hema Murthy

Department of Computer Science and Engineering

M. V. Vinodh

Ashwin Bellur

K. Badri Narayan

Deepali M. Thakare

Anila Susan

N. M. Suthakar

CLUSTER UNITS

CO-ARTICULATION

Design and Development

Indian languages

Listening tests

TEXT TO SPEECH SYNTHESIS

UNIT SELECTION

Linguistics

Query languages

Speech synthesis

Synthesis (chemical)

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Using polysyllabic units for text to speech synthesis in Indian languages

Proceedings of 16th National Conference on Communications, NCC 2010

HMM based speech synthesis (HTS) is a state-of-the art approach to text-to-speech synthesis. Segmentation of the training data is essential for building any text-to-speech system. Most conventional text-to-speech systems use phones as the basic unit of synthesis and use a speech recogniser to automatically segment the data at the phone level. As Indian languages are low resource languages, accurate transcriptions are difficult to obtain owing to paucity of data. Manual labeling at the phone level is not only laborious but also inaccurate. HMM based flat start segmentation doesn't work well at the sentence level. In this paper we propose an event driven approach to obtain better phone boundaries. Syllable-like events are detected in the speech signal and matched with syllabified transcription of the text. The syllables are converted to phoneme sequences and Baum-Welch embedded re-estimation is restricted to the syllable-level. Subjective evaluations indicate that the proposed system has a lower word error rate compared to that of a conventional system that uses flat start for obtaining phone boundaries. © 2014 IEEE.

2014 20th National Conference on Communications, NCC 2014

Group delay based phone segmentation for HTS

Text, Speech and Language Technology

An Introduction to Text-to-Speech Synthesis

Journal	Data powered by TypesetProceedings of 16th National Conference on Communications, NCC 2010
Publisher	Data powered by TypesetIEEE
Open Access	No