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- W2551291298 abstract "Speech recognition is always looked upon as a fascinating field in human computer interaction. It is one of the fundamental steps towards understanding human cognition and their behavior. This paper explicates the theory and implementation of Automatic Speech Recognition (ASR), in the form of speaker-dependent limited vocabulary isolated word recognizer (IWR). Any IWR contains two main phases, training phase and the testing phase. In the training phase feature extractor transforms the raw speech signal into a compact but effective representation and the extracted features are stored in the database. During the recognition phase the features are extracted by the same or different techniques and are compared with the stored one in the database (1). In the purposed IWR the features of the speech are extracted as LPCC, Mel-frequency Cepstrum coefficients (MFCC), delta mfcc (DMFCC) and delta-delta mfcc (DDMFCC). Vector Quantization (VQ)(3) is used for word modeling process. The final recognition decision is made based on the matching score: word model with the smallest matching score is selected as a word of the test speech sample. Word recognition rate was observed to be 96% with the 20 MFCC coefficients and as we increase the feature vector size to 36 by including DMFCC and DDMFCC recognition rate increase to 99.3%. Better performances could be seen when applying this approach itself or mixed with Hidden Markov Model (HMM) in isolated-word speech recognition." @default.
- W2551291298 created "2016-11-30" @default.
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- W2551291298 date "2007-01-01" @default.
- W2551291298 modified "2023-09-24" @default.
- W2551291298 title "ENHANCED FEATURE VECTOR SET FOR VQ RECOGNIZER IN ISOLATED WORD RECOGNITION" @default.
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