Independent component analysis applied to feature extraction for robust automatic speech recognition
Independent component analysis applied to feature extraction for robust automatic speech recognition
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DOI：
10.1049/el:20001365
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发表时间：
2000-11
影响因子：
1.1
通讯作者：
L. Potamitis;N. Fakotakis;G. Kokkinakis
中科院分区：
文献类型：
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作者：
L. Potamitis;N. Fakotakis;G. Kokkinakis
作者探索了独立分量分析(ICA)作为一种统计技术，用于在自动语音识别(ASR)的背景下为频谱和倒谱的投影得出合适的数据驱动的表征基础。基于语音变异性的独立机制和统计独立性的概念之间的密切联系，他们推导出了一种新的特征变换，从而实现了识别性能的持续改善。
The authors explore independent component analysis (ICA) as a statistical technique for deriving suitable data-driven representational bases for the projection of spectra and cepstra in the context of automatic speech recognition (ASR). Based on the close link between the independent mechanisms of speech variability and the concept of statistical independence they derive a new feature transformation that effects consistent improvement in recognition performance.