Abstract

Long speech-text alignment can facilitate large-scale study of rich spoken language resources that have recently become widely accessible, eg, collections of audio books, or multimedia documents. For such resources, the conventional Viterbibased forced alignment may often be proven inadequate mainly due to mismatched audio and text and/or noisy audio. In this paper, we present SailAlign which is an open-source software toolkit for robust long speech-text alignment that circumvents these restrictions. It implements an adaptive, iterative speech recognition and text alignment scheme that allows for the processing of very long (and possibly noisy) audio and is robust to transcription errors. SailAlign is evaluated on artificially created long chunks of the TIMIT database. Audio is artificially contaminated with babble noise, and the corresponding transcriptions are corrupted at various levels. We present the corresponding word boundary detection results. Finally, we demonstrate the potential use of the software for the exploitation of audio books for the study of read speech.

Date

2011

Authors

Athanasios Katsamanis, Matthew Black, Panayiotis G Georgiou, Louis Goldstein, Shrikanth Narayanan

Journal

Proc. of workshop on new tools and methods for very-large scale phonetics research

Volume

1