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- W2990166125 abstract "Detecting voice commands accurately are not simple tasks. Still, voice recognition applications are needed, i.e., voice instruction for devices in a smart home. To recognize well, many sample sounds must be used for training. To avoid the limitation of collecting many sampled sounds, the on-site training by the sound of smartphone users could be an alternative. ‘Sound Tooth’, a voice command mobile application, was designed according to ‘trained by the user’ concept. It helps dentists to record school children oral health examination. To record them, the dentists provide short selected tooth status in a form of chosen isolated words, either in Thai or English. For this work, we applied the voice recognizer, named CMU PocketSphinx API. The system has been tested two ways: SphinxTrainer and participants’ testing. The system training result reports the WER = 8.8% (~ 91% accuracy). In the participants’ test, we compare the system using the speaker independent acoustic model to individual’s one. The test results show the accuracy can be improved from 68% in speaker independent model to 83.3% accuracy for individual one, giving 15% better." @default.
- W2990166125 created "2019-12-05" @default.
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- W2990166125 date "2019-10-01" @default.
- W2990166125 modified "2023-09-26" @default.
- W2990166125 title "Sound Tooth: Mobile Oral Health Exam Recording Using Individual Voice Recognition" @default.
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- W2990166125 doi "https://doi.org/10.1109/incit.2019.8911926" @default.
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