TDNN labeling for a HMM recognizer

Author

Ma, Weiye ; Compernolle, Dirk Van

Author_Institution

Katholieke Univ., Leuven, Heverlee, Belgium

fYear

1990

fDate

3-6 Apr 1990

Firstpage

421

Abstract

A system which combines the good short-time classification properties of the time delay neural network (TDNN) with the good integration and overall recognition capabilities of hidden Markov models (HMMs) is proposed for a speaker-independent speech recognizer. The standard vector quantization is replaced by a TDNN labeler giving phonelike labels. In order to avoid hand segmentation for the training of the TDNN, a separate HMM and a Viterbi alignment derived from it are used. This gives a coarse phonetic segmentation of the training data

Keywords

Markov processes; neural nets; speech recognition; HMM recognizer; TDNN labeler; TDNN labeling; Viterbi alignment; coarse phonetic segmentation; hidden Markov models; phoneme recognition; speaker-independent speech recognizer; time delay neural network; training data; vector quantization; Computer networks; Delay effects; Hidden Markov models; Labeling; Multi-layer neural network; Neural networks; Power system modeling; Speech recognition; Training data; Vector quantization; Viterbi algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.115728

Filename

115728

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2890866