Investigation of Robust Features for Speech Recognition in Hostile Environments

This paper presents an investigation of robust features for speech recognition in three different noisy environments. The state-of art Mel-frequency cepstral coefficients were extensively explored in additive, convolutive and reverberant environments. These environments have captured the interest of many researches in speech recognition systems. We evaluate robust speech recognition results on the TI-DIGIT database. Significant word error rate reductions were observed in the connected digit recognition experiments. The recognition experiments vindicate the robustness of Mel-frequency cepstral coefficient with dynamic features and cepstral mean normalization in hostile environments, especially additive and reverberant noise