Phonetic subspace adaptation for automatic speech recognition

Author

Ghalehjegh, Sina Hamidi ; Rose, Richard C.

Author_Institution

Electr. & Comput. Eng. Dept., McGill Univ., Montreal, QC, Canada

fYear

2013

Firstpage

7937

Lastpage

7941

Abstract

An approach is proposed for adapting subspace projection vectors in the subspace Gaussian mixturemodel (SGMM) [1]. Subword models in the SGMM are composed of states, each of which are parametrized using a small number of subspace projection vectors. It is shown here that these projection vectors provide a compact and well-behaved characterization of phonetic information in speech. A regression based subspace vector adaptation approach is proposed for adapting these parameters. The performance of this approach is evaluated for unsupervised speaker adaptation on two large vocabulary speech corpora.

Keywords

Gaussian processes; speech recognition; vectors; SGMM; automatic speech recognition; phonetic information; phonetic subspace adaptation; regression based subspace vector adaptation approach; subspace Gaussian mixture model; subspace projection vectors; subword models; unsupervised speaker adaptation; vocabulary speech corpora; Acoustics; Adaptation models; Hidden Markov models; Speech; Speech recognition; Training; Vectors; Phonetic Subspace; Speaker Adaptation;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on

Conference_Location

Vancouver, BC

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2013.6639210

Filename

6639210