Phoneme recognition improvement by restricting training section in concatenated HMM training

Author

Ariki, Y. ; Doi, K.

Author_Institution

Ryukoku Univ., Ohtsu, Japan

Volume

i

fYear

1994

fDate

19-22 Apr 1994

Abstract

Conventional concatenated training of phoneme HMMs can approximate the output probability structure of the HMMs. However, it causes an essential problem that the output probability structure of the HMMs becomes blurred, because HMMs are trained over the whole speech data of a given sentence. This paper describes a method to solve this problem and to make the output probability structure sharp, by restricting the training section to the proper section associated with the phoneme, still keeping the advantages of the conventional concatenated HMM training. Four kinds of experiments were carried out and the proposed method showed a 5.6% improvement of the recognition rate of the phonemes included in the continuously spoken sentences, compared to the conventional concatenated phoneme HMM training

Keywords

hidden Markov models; probability; speech processing; speech recognition; concatenated HMM training; continuously spoken sentences; output probability structure; phoneme recognition improvement; restricting training section; Concatenated codes; Databases; Education; Hidden Markov models; Semisupervised learning; Speech; Supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389307

Filename

389307