Data-driven analysis of speech

被引：0
|
作者：
Hermansky, H [1 ]
机构：
[1] Oregon Grad Inst, Portland, OR 97291 USA
[2] Int Comp Sci Inst, Berkeley, CA 94704 USA
来源：
TEXT, SPEECH AND DIALOGUE | 1999年 / 1692卷
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：
We show on results taken from recent studies from our laboratory that conventional speech analysis techniques for ASR (such as Mel cepstrum or PLP) in combination with dynamic features (such as estimates of derivatives of cepstral feature trajectories) are sub-optimal and could be improved. The improvements can be derived by employing large labeled databases which allow for studying how is the linguistic information distributed in time and in frequency as well as for a design of discrimitative spectral basis and temporal RASTA filters.
引用
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页码：10 / 18
页数：9
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