Robust mapping of noisy speech parameters for HMM word spotting

It is demonstrated that using the proposed probabilistic vector mapping algorithm as a feature preprocessor results in robust performance levels across a wide range of signal-to-noise (SNR) levels. The authors evaluate the algorithm using an HMM word spotting system trained with clean cepstral features and tested with vector mapped noisy cepstra. In addition to robust behavior, it is shown that using the vector mapper results in performance that equals or exceeds that of using matched training and testing. For example, with 10-dB SNR testing speech, word spotting performance with the vector mapping preprocessor and clean training is 15% better than matching training with 10-dB SNR speech. A mapping algorithm based on the method of radial basis functions (RBFs) for mapping noisy speech features into the space of clean features is presented. Performance using this RBF mapper is shown to be comparable to that of the vector mapper