On the Study of Energy-Based Speech Feature Normalization and Application to Voice Activity Detection

[[abstract]]This thesis considered robust speech recognition in various noise environments, with a special focus on investigating the ways to reconstruct the clean time-domain log-energy features from the noise-contaminated ones. Based on the distribution characteristics of the log-energy features of each speech utterance, we aimed to develop an efficient approach to rescale the log-energy features of the noisy speech utterance so as to alleviate the mismatch caused by environmental noises for better speech recognition performance. As the time-domain phenomena of the speech signals reveal that lower-energy speech frames are more vulnerable to additive noises than higher-energy ones, and that the magnitudes of the log-energy features of the speech utterance tend to be lifted up when they are seriously interfered with additive noise, we therefore proposed a simple but effective approach, named log-energy rescaling normalization (LERN), to appropriately rescale the log-energy features of noisy speech to that of the desirable clean one. The speech recognition experiments were conducted under various noise conditions using the European Telecommunications Standards Institute (ETSI) Aurora-2.0 database. The database contains a set of connected digit utterances spoken in English. It offers eight noise sources and seven different signal-to-noise ratios (SNRs). The experiment results showed that the performance of the proposed LERN approach was considerably better than the other conventional energy or log-energy feature normalization methods. Another set of experiments conducted on the large vocabulary continuous speech recognition (LVCSR) of Mandarin broadcast news also evidenced the effectiveness of LERN.