Robust speech recognition in noise using adaptation and mapping techniques

This paper compares three techniques for recognizing continuous speech in the presence of additive car noise: (1) transforming the noisy acoustic features using a mapping algorithm, (2) adaptation of the hidden Markov models (HMMs), and (3) combination of mapping and adaptation. To make the signal processing robust to additive noise, we apply a technique called probabilistic optimum filtering. We show that at low signal-to-noise ratio (SNR) levels, compensating in the feature and model domains yields similar performance. We also show that adapting the HMMs with the mapped features produces the best performance. The algorithms were implemented using SRI's DECIPHER speech recognition system and were tested on the 1994 ARPA-sponsored CSR evaluation test spoke 10.