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This paper focuses on developing IndoVMS, an application to dictate a short message for Android smartphones using Indonesian language. It is developed using Pocketsphinx and Java. Testing to five speakers, three males and two females, shows that the word error rate is so high for statistical language model and quite low for rule-based grammar. Investigating(More)
This paper discusses an implementation of CMU Sphinx-4 in an automatic speech recognition-based information center (ASRIC) for Indonesian language. The ASRIC uses a vector space model (VSM) to improve the performance of statistical language model and to recognize the user utterance more flexibly. Testing to an Indonesian speaker shows that VSM is capable of(More)
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