Overlapped speech detection in meeting using cross-channel spectral subtraction and spectrum similarity
Ryo Yokoyama, Yu Nasu, Koichi Shinoda, Koji Iwano · 2012
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.