Parkinsons disease Diagnosis using Mel frequency Cepstral Coefficients and Vector Quantization
نویسندگان
چکیده
منابع مشابه
Speaker Identification and Verification using Vector Quantization and Mel Frequency Cepstral Coefficients
In the study of speaker recognition, Mel Frequency Cepstral Coefficient (MFCC) method is the best and most popular which is used to feature extraction. Further vector quantization technique is used to minimize the amount of data to be handled in recent years. In the present study, the Speaker Recognition using Mel Frequency Cepstral coefficients and vector Quantization for the letter “Zha” (in ...
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In order to develop the assessment of speech disorders for detecting patients with Parkinson’s disease (PD), we have collected 34 sustained vowel / a /, from 34 subjects including 17 PD patients. We subsequently extracted from 1 to 20 coefficients of the Mel Frequency Cepstral Coefficients (MFCCs) from each individual. To extract the voiceprint from each individual, we compressed the frames by ...
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We examine in some detail Mel Frequency Cepstral Coefficients (MFCCs) the dominant features used for speech recognition and investigate their applicability to modeling music. In particular, we examine two of the main assumptions of the process of forming MFCCs: the use of the Mel frequency scale to model the spectra; and the use of the Discrete Cosine Transform (DCT) to decorrelate the Mel-spec...
متن کاملRecognition Of Voice Using Mel Cepstral Coefficient & Vector Quantization
Human Voice is characteristic for an individual. The ability to recognize the speaker by his/her voice can be a valuable biometric tool with enormous commercial as well as academic potential. Commercially, it can be utilized for ensuring secure access to any system. Academically, it can shed light on the speech processing abilities of the brain as well as speech mechanism. In fact, this feature...
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Filter bank is the most common feature being employed in the research of the marginalisation approaches for robust speech recognition due to its simplicity in detecting the unreliable data in the frequency domain. In this paper, we propose a hybrid approach based on the marginalisation and the soft decision techniques that make use of the Mel-frequency cepstral coefficients (MFCCs) instead of f...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2011
ISSN: 0975-8887
DOI: 10.5120/1821-2393