A comparison of front-end compensation strategies for robust LVCSR under room reverberation and increased vocal effort

Automatic speech recognition is known to deteriorate in the presence of room reverberation and variation of vocal effort in speakers. This study considers robustness of several state-of-the-art front-end feature extraction and normalization strategies to these sources of speech signal variability in the context of large vocabulary continuous speech recognition (LVCSR). A speech database recorded in an anechoic room, capturing modal speech and speech produced at different levels of vocal effort, is reverberated using measured room impulse responses and utilized in the evaluations. It is shown that the combination of recently introduced mean Hilbert envelope coefficients (MHEC) and a normalization strategy combining cepstral gain normalization and modified RASTA filtering (CGN RASTALP) provides considerable recognition performance gains for reverberant modal and high vocal effort speech.