Developing speech recognition systems for corpus indexing under the IARPA Babel program
Jia Cui, Xiaodong Cui, Bhuvana Ramabhadran, Janice Kim, Brian E. D. Kingsbury, Jonathan Mamou, Lidia Mangu, Michael Picheny, Tara N. Sainath, Abhinav Sethy · 2013
Automatic speech recognition is a core component of many applications, including keyword search. In this paper we describe experiments on acoustic modeling, language modeling, and decoding for keyword search on a Cantonese conversational telephony corpus collected as part of the IARPA Babel program. We show that acoustic modeling techniques such as the bootstrapped-and-restructured model and deep neural network acoustic model significantly outperform a state-of-the-art baseline GMM/HMM model, in terms of both recognition performance and keyword search performance, with improvements of up to 11% relative character error rate reduction and 31% relative maximum term weighted value improvement. We show that while an interpolated Model M and neural network LM improve recognition performance, they do not improve keyword search results; however, the advanced LM does reduce the size of the keyword search index. Finally, we show that a simple form of automatically adapted keyword search performs 16% better than a preindexed search system, indicating that out-of-vocabulary search is still a challenge.