M. Akbacak, D. Vergyri, and A. Stolcke, “Open-vocabulary spoken term detection using graphone-based hybrid recognition systems,” in Proc. 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 5240–5243.
Abstract
We address the problem of retrieving out-of-vocabulary (OOV) words/queries from audio archives for spoken term detection (STD) task. Many STD systems use the output of an automatic speech recognition (ASR) system which has a limited and fixed vocabulary, and are not capable of detecting rare words of high information content, such as named entities. Since such words are often of great interest for a retrieval task it is important to index spoken archives in a way that allows a user to search an OOV query/term. In this work, we employ hybrid recognition systems which contain both words and subword units (graphones) to generate hybrid lattice indexes. We use a word-based STD system as our baseline, and present improvements by employing our proposed hybrid STD system that uses words plus graphones on the English broadcast news genre of the 2006 NIST STD task.