Robust distant-talking speech recognition
Qiushi Lin, C. Che, D.-S. Yak, Longbin Jin, Ben DeVries, John C. Pearson, James L. Flanagan · 1996
Most contemporary speech recognizers are designed to operate with close-talking speech and they work best in a quiet laboratory condition. There is an apparent need to render environment robustness to these systems. The objective of the paper is to explore utility of existing speech recognition technology in adverse "real-world" environments for distant-talking applications. A synergistic system consisting of microphone array and neural network (MANN) is utilized to mitigate environmental interference introduced by reverberation, ambient noise, and channel mismatch between training and testing conditions. The MANN system is evaluated with experiments on continuous distant-talking speech recognition. The results show that the MANN system elevates the word recognition accuracy to a level which is competitive with a retrained speech recognizer and that the neural network compensation performs better than some previously researched techniques.