In this correspondence, a new method of analysis of speech is proposed that will bring out variations in vocal tract system characteristics in short (2-4 ins) segments. In this method, the source and system components of the speech signal are suitably windowed to reduce the effects of truncation of conventional waveform windowing. © 1996 IEEE.

P. Satyanarayana Murthy

Bandwidth

Correlation methods

signal processing

Speech synthesis

Waveform analysis

SOURCE SYSTEM WINDOWING

TRUNCATION

WAVEFORM WINDOWING

Speech analysis

Fulltext

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

Source-system windowing for speech analysis and synthesis

IEEE Transactions on Speech and Audio Processing

In this paper we propose a method for enhancement of speech in the presence of additive noise. The objective is to selectively enhance the high signal-to-noise ratio (SNR) regions in the noisy speech in the temporal and spectral domains, without causing significant distortion in the resulting enhanced speech. This is proposed to be done at three different levels. (a) At the gross level, by identifying the regions of speech and noise in the temporal domain. (b) At the finer level, by identifying the regions of high and low SNR portions in the noisy speech. (c) At the short-time spectrum level, by enhancing the spectral peaks over spectral valleys. The basis for the proposed approach is to analyze linear prediction (LP) residual signal in short (1-2 ms) segments to determine whether a segment belongs to a noise region or speech region. In the speech regions the inverse spectral flatness factor is significantly higher than in the noisy regions. The LP residual signal enables us to deal with short segments of data due to uncorrelatedness of the samples. Processing of noisy speech for enhancement involves mostly weighting the LP residual signal samples. The weighted residual signal samples are used to excite the time-varying all-pole filter to produce enhanced speech. As the additive noise level in the speech signal is increased, the quality of the resulting enhanced speech decreases progressively due to loss of speech information in the low SNR, high noise regions. Thus the degradation in performance of enhancement is graceful as the overall SNR of the noisy speech is decreased.

Speech Communication

Speech enhancement using linear prediction residual

IEEE Transactions on Biomedical Engineering

Measuring and modeling vocal source-tract interaction

Some problems in voice source analysis

IEEE Transactions on Signal Processing

Glottal source estimation using a sum-of-exponentials model

Journal of the International Phonetic Association

Douglas O'Shaughnessy, Speech Communication: Human and Machine. Reading, Massachusetts: Addison-Wesley Publishing Company, 1987. Pp. xviii + 568. ISBN 0-201-16520-1.

Journal	IEEE Transactions on Speech and Audio Processing
ISSN	10636676
Open Access	Yes