Speech recognition using filter-bank features

被引：0

作者：

Ravindran, S ^{[1
]}

Demiroglu, C ^{[1
]}

Anderson, DV ^{[1
]}

机构：

[1] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA

来源：

CONFERENCE RECORD OF THE THIRTY-SEVENTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, VOLS 1 AND 2 | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Mel-frequency cepstral coefficients (MFCC) have been shown to be very useful in tasks of speech recognition and are the preferred features in state of the art speech recognition systems. We present features derived from filter bank outputs whose performance is comparable to that of MFCCs for connected digit recognition using a Hidden Markov Model (HMM) based speech recognition system. The feature extraction method we present is easily implementable in floating gate analog VLSI circuitry which makes it a viable option for low power speech recognition tasks.

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页码：1900 / 1903

页数：4

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