Dominance detection in a reverberated acoustic scenario
ISNN (1)(2012)
摘要
This work proposes a dominance detection framework operating in reverberated environments. The framework is composed of a speech enhancement front-end, which automatically reduces the distortions introduced by room reverberation in the speech signals, and a dominance detector, which processes the enhanced signals and estimates the most and least dominant person in a segment. The front-end is composed by three cooperating blocks: speaker diarization, room impulse responses identification and speech dereverberation. The dominance estimation algorithm is based on bidirectional Long Short-Term Memory networks which allow for context-sensitive activity classification from audio feature functionals extracted via the real-time speech feature extraction toolkit openSMILE. Experiments have been performed suitably reverberating the DOME dataset: the absolute accuracy improvement averaged over the addressed reverberated conditions is 32.68% in the most dominant person estimation task and 36.56% in the least dominant person estimation one, both with full agreement among annotators.
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关键词
dominant person estimation task,dominance detection framework operating,speech dereverberation,acoustic scenario,dominant person estimation,dominance estimation algorithm,speech signal,speech enhancement front-end,dominance detector,real-time speech feature extraction,dominant person
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