An information-theoretic perspective on feature selection in speaker recognition
An information-theoretic perspective on feature selection in speaker recognition
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DOI：
10.1109/lsp.2005.849495
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发表时间：
2005-07-01
影响因子：
3.9
通讯作者：
Lee, C
中科院分区：
文献类型：
--
作者：
Eriksson, T;Kim, S;Lee, C
本文从信息论的角度研究说话人识别中的特征选择问题。我们密切联系的性能，在预期的分类错误概率，扬声器的身份和功能之间的互信息。信息论可以帮助我们对特征选择和性能进行定性描述。我们研究了用于说话人识别的各种常见特征，如梅尔翘曲倒谱系数和线性预测系数的各种参数化。理论和实验结果为说话人识别应用中的特征选择和性能提供了有价值的见解。
This letter studies feature selection in speaker recognition from an information-theoretic view. We closely tie the performance, in terms of the expected classification error probability, to the mutual information between speaker identity and features. Information theory can then help us to make qualitative statements about feature selection and performance. We study various common features used for speaker recognition, such as mel-warped cepstrum coefficients and various parameterizations of linear prediction coefficients. The theory and experiments give valuable insights in feature selection and performance of speaker-recognition applications.