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In enclosed environments where robots are deployed, the observed speech signal is smeared due to reverberation. This degrades the performance of the automatic speech recognition (ASR). Thus, hands-free speech recognition for human-machine communication is a difficult task. Most speech enhancement techniques used to address this problem enhance the contaminated waveform independent from that of the...
Speech enhancement is a common approach to address the effects of degradation due to noise and channel contamination. This approach is intended to suppress unwanted signal and recover the clean speech. In this paper, we focus on two simple and low-computational methods: Wiener filtering (WF) and spectral subtraction (SS). Conventionally, these are formulated with no relation with automatic speech...
In real-time speech recognition applications, there is a need to implement a fast and reliable adaptation algorithm. We propose a method to reduce adaptation time of the unsupervised speaker adaptation based on HMM-sufficient statistics. We use only a single arbitrary utterance without transcriptions in selecting the N-best speakers' sufficient statistics created offline to provide data for adaptation...
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