Overlapped Speech Detection in Meeting Using Cross-Channel Spectral Subtraction and Spectrum Similarity

We propose an overlapped speech detection method for speech recognition and speaker diarization of meetings, where each speaker wears a lapel microphone. Two novel features are utilized as inputs for a GMM-based detector. One is speech power after cross-channel spectral subtraction which reduces the power from the other speakers. The other is an amplitude spectral cosine correlation coefficient which effectively extracts the correlation of spectral components in a rather quiet condition. We evaluated our method using a meeting speech corpus of four speakers. The accuracy of our proposed method, 74.1%, was significantly better than that of the conventional method, 67.0%, which uses raw speech power and power spectral Pearson’s correlation coefficient.