Soft-feature decoding for speech recognition over wireless channels

A distributed automatic speech recognition (ASR) system is considered where features of the speech signal are extracted at the wireless terminal and transmitted to a centralized ASR server. An unequal error protection scheme is used for the quantized ASR feature stream. At the receiver, coherent demodulation is performed and the probability of error for each bit is computed using the max-log MAP algorithm. A 'soft-feature' decoding strategy is introduced at the ASR server that uses the marginal distribution of only the reliable features during likelihood computation. Alternatively, the confidence of each feature is computed from the bit error probabilities and each feature in the probability computation is weighted as a function of the feature confidence. The performance of the proposed soft-feature algorithms is evaluated over typical cellular wireless channels and it is shown to reduce ASR error rate by over 50% for certain channels at a small additional computational cost