Using talker location to detect spurious utterances in desktop command and control

Hands-free desktop command and control speech recognition suffers from the critical drawback of improperly rejecting spurious conversation. This results in false acceptances of unintended speech commands that can inconvenience the user. A neural-network approach is proposed to detect spurious conversation by determining talker location. The approach is based on the premise that spoken utterances not directed towards the microphone source tend to be more reverberant and are likely to be spurious. The method estimates a confidence measure proportional to the amount of reverberation in the end-pointed speech signal. The measure is obtained from a neural network that determines if the speech signal was directed to the microphone or was spoken otherwise. The proposed measure can be combined with the acoustic, linguistic and semantic information to improve upon decisions taken by conventional rejection modeling schemes