A robust speech recognition system using word-spotting with noise immunity learning
Yoichi Takebayashi, H. Tsuboi, Hiroshi Kanazawa · 1991
A speech recognition system using word-spotting with noise immunity learning has been developed to achieve robust performance under noisy environments. The system employs word-spotting based on the multiple similarity (MS) method for eliminating word boundary detection errors, noise immunity learning for improving noise robustness, and an accelerator for reducing processing time. Noise immunity learning is performed using noisy speech data and noise data. Data from 39 male speakers were used to evaluate the recognition performance; the remaining data were used for the learning. Recognition scores obtained by word-spotting alone and with noise immunity learning were 88.5% and 98.4%, respectively, for an SNR of 10 dB.>