Speech-To-Text Conversion (STT) System Using Hidden Markov Model (HMM)
Su Myat Mon, Hla Myo Tun
Keywords: Speech Recognition, End Point Detection, MFCC, HMM, MATLAB
Abstract: Speech is an easiest way to communicate with each other. Speech processing is widely used in many applications like security devices, household appliances, cellular phones, ATM machines and computers. The human computer interface has been developed to communicate or interact conveniently for one who is suffering from some kind of disabilities. Speech-to-Text Conversion (STT) systems have a lot of benefits for the deaf or dumb people and find their applications in our daily lives. In the same way, the aim of the system is to convert the input speech signals into the text output for the deaf or dumb students in the educational fields. This paper presents an approach to extract features by using Mel Frequency Cepstral Coefficients (MFCC) from the speech signals of isolated spoken words. And, Hidden Markov Model (HMM) method is applied to train and test the audio files to get the recognized spoken word. The speech database is created by using MATLAB.Then, the original speech signals are preprocessed and these speech samples are extracted to the feature vectors which are used as the observation sequences of the Hidden Markov Model (HMM) recognizer. The feature vectors are analyzed in the HMM depending on the number of states.
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