Auditory-Based Features Extraction Method for Speech Recognition
نویسندگان
چکیده
In this paper we present a features extractor for speech recognition. The proposed features extraction method based on auditory filter modelling. The latter uses a Gammachirp Filterbank (GcFB), where their center frequencies are selected according to one of the three scales: the ERB-rate scale, the MEL scale or the BARK scale. The performance of the proposed features is evaluated, in the context of isolated wordsrecognition, on the TIMIT database. The recognition rate of our features extraction method with ERB-rate scale gives interesting results vs. the other two scales. The HTK platform (HMM Toolkit) recognizer is employed for the recognition system task. It’s based on the Hidden Markov Models with Gaussian Mixture densities (HMM-GM). Keywords— Gammachirp auditory filterbank, Features extraction, Speech Recognition
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