Planning, neural networks and Markov models for automatic speech recognition

Author

Mori, Renato De

Author_Institution

Sch. of Comput. Sci., McGill Univ., Montreal, Que., Canada

fYear

1988

Firstpage

395

Abstract

The possibility of programming the execution of multilayered networks is considered. This planned execution produces a coded description of speech segments in terms of degrees of evidence of phonetic features. Continuous-parameter-density hidden Markov models are suggested for modeling the time evolution of feature degrees. Experimental results on plosive sounds, other consonants, and vowels are presented

Keywords

Markov processes; computerised pattern recognition; encoding; neural nets; speech recognition; automatic speech recognition; coded description; consonants; continuous-parameter-density hidden Markov models; evidence; multilayered networks; network programming; neural networks; phonetic features; planning; plosive sounds; speech segments; vowels; Automatic programming; Automatic speech recognition; Computer science; Hidden Markov models; Learning systems; Magnetooptic recording; Neural networks; Speech coding; Vector quantization; Vocabulary;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1988., 9th International Conference on

Conference_Location

Rome

Print_ISBN

0-8186-0878-1

Type

conf

DOI

10.1109/ICPR.1988.28252

Filename

28252

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1713406