Our earlier work [1] on speech synthesis has shown that syllablesproduce reasonably natural quality speech. Nevertheless, artifacts are present due to discontinuities pitch, energy, and formant trajectories at the joining point the units. In this paper, we present some minimal signal techniques for reducing these artifacts.©2008 IEEE.

Hema Murthy

Department of Computer Science and Engineering

C. S. Ramalingam

Department of Electrical Engineering

M.V. Vinodh

Y.R. Venugopalakrishna

LSF INTERPOLATION

Prosody

SILENCE CORRECTION

STOP SOUNDS

SYLLABLE

interpolation

Speech synthesis

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

Methods for improving the quality of syllable based speech synthesis

2008 IEEE Workshop on Spoken Language Technology, SLT 2008 - Proceedings

Case Medical Research

Traumatologic Acute Pain Management With Fentanyl Transdermal Therapeutic System (TTS)

HTS Teologiese Studies / Theological Studies

The expression <img src="http://files.ithuta.net/OpenJournals/HTS/Archives/ID201.png"/> as the key to 1 Peter 2:1-3

Automatic spoken language identification (LID) is the task of identifying the language from a short utterance of the speech signal uttered by an unknown speaker. The most successful approach to LID uses phone recognizers of several languages in parallel [Zissman, M.A., 1996. Comparison of four approaches to automatic language identification of telephone speech. IEEE Trans. Speech Audio Process. 4 (1), 31-44]. The basic requirement to build a parallel phone recognition (PPR) system is segmented and labeled speech corpora. In this paper, a novel approach is proposed for the LID task which uses parallel syllable-like unit recognizers, in a frame work similar to the PPR approach in the literature. The difference is that the sub-word unit models for each of the languages to be recognized are generated in an unsupervised manner without the use of segmented and labeled speech corpora. The training data of each of the languages is first segmented into syllable-like units and language-dependent syllable-like unit inventory is created. These syllable-like units are then clustered using an incremental approach. This results in a set of syllable-like units models for each language. Using these language-dependent syllable-like unit models, language identification is performed based on accumulated acoustic log-likelihoods. Our initial results on the Oregon Graduate Institute Multi-language Telephone Speech Corpus [Muthusamy, Y.K., Cole, R.A., Oshika, B.T., 1992. The OGI multi-language telephone speech corpus. In: Proceedings of Internat. Conf. Spoken Language Process., October 1992, pp. 895-898] show that the performance is 72.3%. We further show that if only a subset of syllable-like unit models that are unique (in some sense) are considered, the performance improves to 75.9%. © 2006 Elsevier B.V. All rights reserved.

Speech Communication

Language identification using acoustic log-likelihoods of syllable-like units

A comparison of spectral smoothing methods for segment concatenation based speech synthesis

AJS Review

Robert Liberles. Salo Wittmayer Baron: Architect of Jewish History. New York: New York University Press, 1995. xiii, 425 pp.

Journal	Data powered by Typeset2008 IEEE Workshop on Spoken Language Technology, SLT 2008 - Proceedings
Publisher	Data powered by TypesetIEEE
Open Access	No