Combining State-Level Spotting and Posterior-Based Acoustic Match for Improved Query-by-Example Spoken Term Detection

Shuji Oishi, Tatsuya Matsuba, Mitsuaki Makino, Atsuhiko Kai


In spoken term detection (STD) systems, automatic speech recognition (ASR) frontend is often employed for its reasonable accuracy and efficiency. However, out-of-vocabulary (OOV) problem at ASR stage has a great impact on the STD performance for spoken query. In this paper, we propose combining feature-based acoustic match which is often employed in the STD systems for low resource languages, along with the other ASR-derived features. First, automatic transcripts for spoken document and spoken query are decomposed into corresponding acoustic model state sequences and used for spotting plausible speech segments. Second, DTW-based acoustic match between the query and candidate segment is performed using the posterior features derived from a monophone-state DNN. Finally, an integrated score is obtained by a logistic regression model, which is trained with a large spoken document and automatically generated spoken queries as development data. The experimental results on NTCIR-12 SpokenQuery&Doc-2 task showed that the proposed method significantly outperforms the baseline systems which use the subword-level or state-level spotting alone. Also, our universal scoring model trained with a separate set of development data could achieve the best STD performance, and showed the effectiveness of additional ASR-derived features regarding the confidence measure and query length.


DOI: 10.21437/Interspeech.2016-1259

Cite as

Oishi, S., Matsuba, T., Makino, M., Kai, A. (2016) Combining State-Level Spotting and Posterior-Based Acoustic Match for Improved Query-by-Example Spoken Term Detection. Proc. Interspeech 2016, 740-744.

Bibtex
@inproceedings{Oishi+2016,
author={Shuji Oishi and Tatsuya Matsuba and Mitsuaki Makino and Atsuhiko Kai},
title={Combining State-Level Spotting and Posterior-Based Acoustic Match for Improved Query-by-Example Spoken Term Detection},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-1259},
url={http://dx.doi.org/10.21437/Interspeech.2016-1259},
pages={740--744}
}