Proceedings:
Book One
Volume
Issue:
Proceedings of the AAAI Conference on Artificial Intelligence, 20
Track:
Natural Language Processing and Speech Recognition
Downloads:
Abstract:
This paper addresses the problem of detecting keywords in unconstrained speech. The proposed algorithms search for the speech segment maximizing the average observation probability along the most likely path in the hypothesized keyword model. As known, this approach (sometimes referred to as sliding model method) requires a relaxation of the begin/endpoints of the Viterbi matching, as well as a time normalization of the resulting score. This makes solutions complex (i.e., LN
AAAI
Proceedings of the AAAI Conference on Artificial Intelligence, 20