Robust Speech Recognition from Binary Masks

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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2010
Accession Number
AD1001142

Entities

People

  • Arun Narayanan
  • DeLiang Wang

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Automated Speech Recognition
  • Automatic
  • Cognitive Science
  • Cognitive Systems Engineering
  • Computer Science
  • Computers
  • Convolutional Neural Networks
  • Engineering
  • Frequency
  • Index Terms
  • Neural Networks
  • Recognition
  • Stationary
  • Test Sets
  • Training

Fields of Study

  • Computer science

Readers

  • Computer Programming and Software Development.
  • Speech Processing/Speech Recognition.
  • Systems Analysis and Design

Technology Areas

  • AI & ML