AN APPROACH TO SEQUENTIAL GROUPING IN COCHANNEL SPEECH

Speech Enhancement

Presented by: DeLiang Wang, Author(s): Ke Hu, DeLiang Wang, The Ohio State University, United States

Model-based methods for sequential organization in cochannel speech require pretrained speaker models and often prior knowledge of participating speakers. We propose an unsupervised approach to sequential organization of cochannel speech. Based on cepstral features, we first cluster voiced speech into two speaker groups by maximizing the ratio of between- and within-group distances penalized by within-group concurrent pitches. To group unvoiced speech, we employ an onset/offset based analysis to generate time-frequency segments. Unvoiced segments are then labeled by the complementary portions of segregated voiced speech. Our method does not require any pretrained model and is computationally simple. Evaluations and comparisons show that the proposed method outperforms a model-based method in terms of speech segregation.

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