Using Linguistic Information To Detect Overlapping Speech

Overlapping speech is still a major cause of error in many speech processing applications, currently without any satisfactory solution. This paper considers the problem of detecting segments of overlapping speech within meeting recordings. Using an HMM-based framework recordings are segmented into intervals containing non-speech, speech and overlapping speech. New to this contribution is the use of linguistic information, where spoken content is used to improve overlap detection. Using language models for speech and overlap, an overlap score is created for every spoken word and used as an additional feature within the HMM framework. Experiments conducted on the AMI corpus demonstrate the potential of the proposed linguistic features.