High resolution speech feature parametrization for monophone-based stressed speech recognition

被引：43

作者：

Sarikaya, R ^{[1
]}

Hansen, JHL ^{[1
]}

机构：

[1] Univ Colorado, Ctr Spoken Language Res, Robust Speech Proc Lab, Boulder, CO 80309 USA

来源：

IEEE SIGNAL PROCESSING LETTERS | 2000年 / 7卷 / 07期

关键词：

feature extraction; speech recognition; speech under stress; wavelet analysis;

D O I：

10.1109/97.847363

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This letter investigates the impact of stress on monophone speech recognition accuracy and proposes a new set of acoustic parameters based on high resolution wavelet analysis. The two parameter schemes are entitled wavelet packet parameters (WPP) and subband-based cepstral parameters (SBC). The performance of these features is compared to traditional Mel-frequency cepstral coefficients (MFCC) for stressed speech monophone recognition. The stressed speaking styles considered areneutral, angry, loud, and Lombard effect(1) speech from the SUSAS database. An overall monophone recognition improvement of 20.4% and 17.2% is achieved for loud and angry stressed speech, with a corresponding increase in the neutral monophone rate of 9.9% over MFCC parameters.

引用

页码：182 / 185

页数：4

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