Design of Speech Control System IN Car Noise Environments

Author

Ma, Longhua ; Shangguan, Wei ; Zang, Yihua

Author_Institution

Harbin Eng. Univ., Harbin

fYear

2007

fDate

5-8 Aug. 2007

Firstpage

3475

Lastpage

3480

Abstract

Presence of additive noise in speech signals deteriorates the performance of automatic speech recognition systems in cars. For a speech recognition system, we must know where speech and nonspeech segments are. In this paper a new Band Partitioning Spectral Entropy endpoint detection (BPSE) method is used to get the speech start and end point of speech precisely. After that Band Spectral Subtraction (BSS) methods provide in this paper can decrease additive noise obviously. Mel Frequency Cepstral Coefficients (MFCC) are extracted from segmented speech signals. The coefficients are recognized by Hidden Markov Model. The results show that the recognition accuracy can be improved from 39.3% to 95.5%.

Keywords

automobiles; cepstral analysis; hidden Markov models; speech recognition; speech-based user interfaces; band partitioning spectral entropy; band spectral subtraction; car noise; mel frequency cepstral coefficients; speech control system; speech recognition; Additive noise; Automatic control; Automatic speech recognition; Control systems; Entropy; Hidden Markov models; Mel frequency cepstral coefficient; Speech enhancement; Speech recognition; Working environment noise; Band Partitioning Spectra Entropy; Band Spectral Subtract; HMM; MFCC;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Automation, 2007. ICMA 2007. International Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4244-0828-3

Electronic_ISBN

978-1-4244-0828-3

Type

conf

DOI

10.1109/ICMA.2007.4304122

Filename

4304122