  Compared with automatic speech recognition (ASR), the human auditory system
is more adept at handling noise-adverse situations, including environmental
noise and channel distortion. To mimic this adeptness, auditory models have
been widely incorporated in ASR systems to improve their robustness. This paper
proposes a novel auditory model which incorporates psychoacoustics and
otoacoustic emissions (OAEs) into ASR. In particular, we successfully implement
the frequency-dependent property of psychoacoustic models and effectively
improve resulting system performance. We also present a novel double-transform
spectrum-analysis technique, which can qualitatively predict ASR performance
for different noise types. Detailed theoretical analysis is provided to show
the effectiveness of the proposed algorithm. Experiments are carried out on the
AURORA2 database and show that the word recognition rate using our proposed
feature extraction method is significantly increased over the baseline. Given
models trained with clean speech, our proposed method achieves up to 85.39%
word recognition accuracy on noisy data.
