Speech recognition using a strong correlation assumption for the instantaneous spectra

Author

Ming, J. ; O´Boyle, P. ; McMahon, J. ; Smith, F.J.

Author_Institution

Sch. of Electr. Eng. & Comput. Sci., Queen´´s Univ., Belfast, UK

Volume

2

fYear

1996

fDate

3-6 Oct 1996

Firstpage

1061

Abstract

The conventional independence assumption made for the evolving speech spectra is replaced by a strong correlation assumption, which then leads to a new stochastic model. This model implements a nonlinear interpolation between the lower and upper bounds of the joint probability distributions. The advantage of the new model over other correlation-based modelling approaches is that it has a low parameter complexity, the same as that in models based on the independence-assumption. Experiments on a speaker-independent E-set database show the effectiveness of this new modelling approach

Keywords

correlation methods; interpolation; probability; spectral analysis; speech recognition; stochastic processes; correlation-based modelling; evolving speech spectra; instantaneous spectra; joint probability distributions; nonlinear interpolation; parameter complexity; speaker-independent E-set database; speech recognition; stochastic model; strong correlation assumption; Computer science; Covariance matrix; Databases; Hidden Markov models; Interpolation; Parameter estimation; Probability distribution; Speech recognition; Stochastic processes; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on

Conference_Location

Philadelphia, PA

Print_ISBN

0-7803-3555-4

Type

conf

DOI

10.1109/ICSLP.1996.607788

Filename

607788