Abstract
Various experiments have conclusively shown that superior continuous speech recognition performance is obtained when using context-dependent phonemic models. However, we have observed that using an explicit context-dependent phonemic model can yield many transcriptions for a single lexicon entry. In this work, we study the compression of the word transcription dictionaries (WTD) into a more compact form to balance the need between flexibility and reliability. Based on a measure of a likelihood function, a statistical model for an automatic procedure to compress a WTD is developed. The compressed dictionary is then used for sentence recognition in a continuous speech recognition system. Experimental results indicate a substantial improvement of the recognition rate after compression.
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Keywords
- Continuous Speech
- Speaking Rate
- Continuous Speech Recognition
- Phonetic Transcription
- Sentence Recognition
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Mouria-Beji, F. (1998). A statistical model for an automatic procedure to compress a word transcription dictionary. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033335
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DOI: https://doi.org/10.1007/BFb0033335
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