This article presents a novel approach to speaker-adaptive recognition of speech from articulationdisordered speakers without a large amount of adaptation data. An unsupervised, incremental adaptation method is adopted for personalized model adaptation based on the recognized syllables with high recognition confidence from an automatic speech recognition (ASR) system. For articulation pattern discovery, the manually transcribed syllables and the corresponding recognized syllables are associated with each other using articulatory features. The Apriori algorithm is applied to discover the articulation patterns in the corpus, which are then used to construct a personalized pronunciation dictionary to improve the recognition accuracy of the ASR. The experimental results indicate that the proposed adaptation method achieves a syllable error rate reduction of 6.1%, outperforming the conventional adaptation methods that have a syllable error rate reduction of 3.8%. In addition, an average syllable error rate reduction of 5.04% is obtained for the ASR using the expanded pronunciation dictionary.
|Journal||ACM Transactions on Asian Language Information Processing|
|Publication status||Published - 2011 Jun 1|
All Science Journal Classification (ASJC) codes
- Computer Science(all)