Vector Quantization Approach for Speaker Recognition using MFCC and Inverted MFCC
نویسندگان
چکیده
منابع مشابه
Vector Quantization Approach for Speaker Recognition using MFCC and Inverted MFCC
Front-end or feature extractor is the first component in an automatic speaker recognition system. Feature extraction transforms the raw speech signal into a compact but effective representation that is more stable and discriminative than the original signal. Since the front-end is the first component in the chain, the quality of the later components (speaker modeling and pattern matching) is st...
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Speaker recognition is one of the most essential tasks in the signal processing which identifies a person from characteristics of voices . In this paper we accomplish speaker recognition using Mel-frequency Cepstral Coefficient (MFCC) with Weighted Vector Quantization algorithm. By using MFCC, the feature extraction process is carried out. It is one of the nonlinear cepstral coefficient functio...
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Automatic Speech Recognition (ASR) technology is a way to interface with computer. In this paper we describe speech recognition technique using multiple codebooks of MFCC derived features. The proposed algorithm is useful in detecting isolated words of speech. In this algorithm we first create database i.e. codebook by calculating mel frequency cepstral coefficient first and then codeword for e...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2011
ISSN: 0975-8887
DOI: 10.5120/2188-2774