Augmenting Short-term Cepstral Features with Long-term Discriminative Features for Speaker Verification of Telephone Data

Abstract : Short-term cepstral features have long been chosen as standard features for speaker recognition thanks to their relevance and effectiveness. In contrast, discriminative features, calculated by a multi-layer perceptron (MLP) from much longer stretches of time, have been gradually adopted in automatic speech recognition (ASR). It has been shown that augmenting short-term cep-stral features with long-term MLP (multi-layer perceptron) features makes it possible to improve significantly the performance of ASR. In this work, we investigate the possibility of augmenting short-term cepstral features with MLP features in order to improve the performance of text-independent speaker verification. We show, that, even though augmenting cepstral features with MLP features does not directly improve speaker verification performance, reducing the dimension of the augmented features, using principal component analysis (PCA), makes it possible to reduce, relatively, around 12% of the equal error rate (EER). Experiments are performed on telephone data of the 2008 NIST SRE (speaker recognition evaluation) database.
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Cong-Thanh Do, Claude Barras, Viet-Bac Le, Achintya Sarkar. Augmenting Short-term Cepstral Features with Long-term Discriminative Features for Speaker Verification of Telephone Data. Interspeech 2013, Aug 2013, Lyon, France. ⟨hal-01690264⟩

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