Signal modeling techniques in speech recognition

Signal modeling techniques in speech recognition (1993)

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by Joseph W. Picone

Venue:	PROCEEDINGS OF THE IEEE
Citations:	99 - 5 self

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Datum	Value	Source
TITLE	Signal modeling techniques in speech recognition	INFERENCE
AUTHOR NAME	Joseph W. Picone	user correction
ABSTRACT	We have seen three important trends develop in the last five years in speech recognition. First, heterogeneous parameter sets that mix absolute spectral information with dynamic, or time-derivative, spectral information, have become common. Second, similariry transform techniques, often used to normalize and decor-relate parameters in some computationally inexpensive way, have become popular. Third, the signal parameter estimation problem has merged with the speech recognition process so that more sophisticated statistical models of the signal’s spectrum can be estimated in a closed-loop manner. In this paper, we review the signal processing components of these algorithms. These al-gorithms are presented as part of a unified view of the signal parameterization problem in which there are three major tasks: measurement, transformation, and statistical modeling. This paper is by no means a comprehensive survey of all possible techniques of signal modeling in speech recognition. There are far too many algorithms in use today to make an exhaustive survey feasible (and cohesive). Instead, this paper is meant to serve as a tutorial on signal processing in state-of-the-art speech recognition systems and to review those techniques most commonly used. In keeping with this goal, a complete mathematical description of each algorithm has been included in the paper.	user correction
YEAR	1993	INFERENCE
VENUE	PROCEEDINGS OF THE IEEE	user correction
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	1215--1247	INFERENCE
VOLUME	81	INFERENCE
CITATIONS	87 found	ParsCit 1.0