Abstract
We analyze the recognition errors made by a morph-based continuous speech recognition system, which practically allows an unlimited vocabulary. Examining the role of the acoustic and language models in erroneous regions shows how speaker adaptive training (SAT) and discriminative training with minimum phone frame error (MPFE) criterion decrease errors in different error classes. Analyzing the errors with respect to word frequencies and manually classified error types reveals the most potential areas for improving the system.
Cite
CITATION STYLE
Hirsimäki, T., & Kurimo, M. (2009). Analysing recognition errors in unlimited-vocabulary speech recognition. In NAACL-HLT 2009 - Human Language Technologies: 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers (pp. 193–196). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1620853.1620906
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