Robust speech recognition from binary masks.
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.
Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA. narayaar@cse.ohio-state.edu
Arun
A
Narayanan
DeLiang
D
Wang
D000465:Algorithms; D006801:Humans; D009622:Noise; D010700:Phonetics; D015203:Reproducibility of Results; D012985:Software Design; D049250:Speech Recognition Software / Q000592:standards*