Weifeng Li, Longbiao Wang, Yicong Zhou, H. Bourlard, Q. Liao
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引用次数: 4
Abstract
The log-energy parameter, typically derived from a full-band spectrum, is a critical feature commonly used in automatic speech recognition (ASR) systems. However, log-energy is difficult to estimate reliably in the presence of background noise. In this paper, we theoretically show that background noise affects the trajectories of not only the “conventional” log-energy, but also its delta parameters. This results in a poor estimation of the actual log-energy and its delta parameters, which no longer describe the speech signal. We thus propose a new method to estimate log-energy from a sub-band spectrum, followed by dynamic change enhancement and mean smoothing. We demonstrate the effectiveness of the proposed log-energy estimation and its post-processing steps through speech recognition experiments conducted on the in-car CENSREC-2 database. The proposed log-energy (together with its corresponding delta parameters) yields an average improvement of 32.8% compared with the baseline front-ends. Moreover, it is also shown that further improvement can be achieved by incorporating the new Mel-Frequency Cepstral Coefficients (MFCCs) obtained by non-linear spectral contrast stretching.
期刊介绍:
The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.