A frequency-weighted HMM based on minimum error classification for noisy speech recognition

As a noise robust HMM, we previously proposed a frequency-weighted HMM (HMM-FW) whose covariance matrices are replaced by the inverse of frequency-weighting matrices. In this HMM, the frequency-weighting parameters were common to all classes and states, and were experimentally adjusted. In order to achieve further noise robustness, this paper examines the class- and state-dependent weighting parameters and their minimum error classification training (MCE) of their weighting characteristics. Using the NOISEX-92 database the MCE-trained HMM-FWs are shown to be more robust even under untrained noise conditions than both the previous HMM-FW and conventional HMM