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Development of HMM-based snoring recognition system for web services

This study presents a Hidden Markov Models(HMM)-based snoring recognition system over the web services environment that consists of the snoring model generation, the recognition system, and the remote device. In design phase, a set of HMM model (snoring and non-snoring) is created from the MFCC feature vectors extracted from the sound corpus consisting of snoring sounds and non-snoring sounds. The recognition system is organized to provide the web services that can be called by the remote device in different platforms with any language. In the test, this system shows that snoring and non-snoring sounds were recognized as 93% and 95.2% for speaker-independent case and 98.3% and 99% for the speaker-dependent case, respectively.