Robust speech recognition from binary masks

Arun Narayanana)
Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210
narayaar@cse.ohio-state.edu
DeLiang Wang
Department of Computer Science and Engineering, and Center for Cognitive Science, The Ohio State University,
Columbus, Ohio 43210
dwang@cse.ohio-state.edu
Abstract: Inspired by recent evidence that a binary pattern may provide
sufficient information for human speech recognition, this letter proposes a
fundamentally different approach to robust automatic speech recognition.
Specifically, recognition is performed by classifying binary masks corresponding
to a word utterance. The proposed method is evaluated using a subset
of the TIDigits corpus to perform isolated digit recognition. Despite dramatic
reduction of speech information encoded in a binary mask, the
proposed system performs surprisingly well. The system is compared with a
traditional HMM based approach and is shown to perform well under low
SNR conditions.
© 2010 Acoustical Society of America
PACS numbers: 43.72.Ne, 43.72.Bs [DOS]
Date Received: August 16, 2010 Date Accepted: September 15, 2010