Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

The Analysis of Influence Factors and Identification of Speaker-Dependent Primi Speech Recognition

Authors
Lin Guo, Yang Bai, Jie Su, Wen-lin Pan, Tian-jun Zhang
Corresponding Author
Lin Guo
Available Online January 2016.
DOI
https://doi.org/10.2991/icaita-16.2016.4How to use a DOI?
Keywords
isolated-word; primi language; HMM; speaker-dependent speech recognition
Abstract
The research object is an endangered minority language of Primi on the southern dialect. This paper takes isolated-word of Primi as the primitive and uses large vocabulary of speech corpora based on HTK. The effect of different quantity of vocabularies, the sample ratio of training and testing, and the data quality on recognition rate of speaker-dependent were investigated. In addition, the identification of speaker-dependent acoustic model were compared. The experimental results show that the high quality data can improve the recognition rate and has little effect on the recognition rate in the vocabulary size.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Series
Advances in Intelligent Systems Research
Publication Date
January 2016
ISBN
978-94-6252-162-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/icaita-16.2016.4How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Lin Guo
AU  - Yang Bai
AU  - Jie Su
AU  - Wen-lin Pan
AU  - Tian-jun Zhang
PY  - 2016/01
DA  - 2016/01
TI  - The Analysis of Influence Factors and Identification of Speaker-Dependent Primi Speech Recognition
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 14
EP  - 17
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.4
DO  - https://doi.org/10.2991/icaita-16.2016.4
ID  - Guo2016/01
ER  -