Work

Robust speech recognition from binary masks

Year: 2010

Type: article

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.

Source: The Journal of the Acoustical Society of America

DOI: 10.1121/1.3497358

Authors: Arun Narayanan, DeLiang Wang

Institution: The Ohio State University

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, Air Force Office of Scientific Research

Awards: G1523888516, Collaborative Research: Separating Speech from Speech Noise to Improve Speech Intelligibility