A Computational Auditory Scene Analysis System for Speech Segregation and Robust Speech Recognition
Abstract
A conventional automatic speech recognizer does not perform well in the presence of multiple sound sources, while human listeners are able to segregate and recognize a signal of interest through auditory scene analysis. We present a computational auditory scene analysis system for separating and recognizing target speech in the presence of competing speech ornoise. We estimate, in two stages, the ideal binary time-frequency (T-F) mask which retainsthe mixture in a local T-F unit if and only if the target is stronger than the interference within the unit. In the first stage, we use harmonicity to segregate the voiced portions of individual sources in each time frame based on multipitch tracking. Additionally, unvoiced portions are segmented based on an onset/offset analysis. In the second stage, speaker characteristics are used to group the T-F units across time frames. The resulting masks are used in an uncertainty decoding framework for automatic speech recognition. We evaluate our system on a speech separation challenge and show that our system yields substantial improvement over the baseline performance.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2007
- Accession Number
- AD1001212
Entities
People
- DeLiang Wang
- Soundararajan Srinivasan
- Yang Shao
- Zhaozhang Jin
Organizations
- Ohio State University