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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
In search of better pronunciation models for speech recognition
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
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Alternative speech communication system for persons with severe speech disorders
EURASIP Journal on Advances in Signal Processing - Special issue on analysis and signal processing of oesophageal and pathological voices
Pronunciation Modeling With Reduced Confusion for Mandarin Chinese Using a Three-Stage Framework
IEEE Transactions on Audio, Speech, and Language Processing
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IEEE Transactions on Audio, Speech, and Language Processing
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Expert Systems with Applications: An International Journal
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This article presents a novel approach to speaker-adaptive recognition of speech from articulation-disordered 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.