Interspeech'2005 - Eurospeech

Lisbon, Portugal
September 4-8, 2005

Improved MLP Structures for Data-Driven Feature Extraction for ASR

Qifeng Zhu, Barry Y. Chen, Frantisek Grezl, Nelson Morgan

International Computer Science Institute, USA

In this paper, we present our recent progress on multi-layer perceptron (MLP) based data-driven feature extraction using improved MLP structures. Four-layer MLPs are used in this study. Different signal processing methods are applied before the input layer of the MLP. We show that the first hidden layer of a four-layer MLP is able to detect some basic patterns from the time-frequency plane. KLT-based dimension reduction along time is applied as a modulation frequency filter. The new feature extraction was tested on a large vocabulary continuous speech recognition (LVCSR) task using the NIST 2001 evaluation set. We achieved 11.6% relative word error rate (WER) reduction compared to the traditional PLP-based baseline feature. This is also a significant improvement compared to our previously published results on the same task using MLP-based features with three-layer MLPs.

Full Paper

Bibliographic reference.  Zhu, Qifeng / Chen, Barry Y. / Grezl, Frantisek / Morgan, Nelson (2005): "Improved MLP structures for data-driven feature extraction for ASR", In INTERSPEECH-2005, 2129-2132.