Hardware Implementation of Speech Recognition Using MFCC and Euclidean Distance
نویسنده
چکیده
This paper suggests Digital Signal processor (DSP) based speech recognition system with improved performance in terms of recognition accuracies and computational cost. The comprehensive surrey of various approaches of feature extraction like Mel filter banks with Mel Frequency Cepstrum Coefficients (MFCC). This paper describes an approach of isolated speech recognition by Digital Signal Processor TMS320C6713 using Mel scale Frequency Cepstral Coefficients and Euclidean distance. Several features are extracted from speech signal of spoken words. An experiments database of total five speakers, speaking 5-10 words each is collected under acoustically controlled room is taken. MFCC are extracted from speech signal of spoken words. To compare inter speaking differences Euclidean distance is used
منابع مشابه
Speaker Dependent Word Recognition Using MFCC and VQ
The paper present effective method for recognition of digit, numbers. Most of speech recognition systems contain two main modules as follow “feature extraction” and “feature matching”. In this project, (MFCC) Mel Frequency Cepstrum coefficient algorithm is used to simulate feature extraction module. Using this algorithm, the Cepstral Coefficients are calculated on Mel frequency scale. VQ (vecto...
متن کاملReal Time Speech Recognition Using DSK TMS320C6713
Speech recognition is an important field of digital signal processing. Automatic Speaker Recognition (ASR) objective is to extract features, characterize and recognize speaker. Mel Frequency Cepstral Coefficients (MFCC) is most widely used feature vector for ASR. MFCC is used for designing a text dependent speaker identification system. In this paper the DSP processor TMS320C6713 with Code Comp...
متن کاملImproving the performance of MFCC for Persian robust speech recognition
The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...
متن کاملImplementation of Face Recognition Algorithm on Fields Programmable Gate Array Card
The evolution of today's application technologies requires a certain level of robustness, reliability and ease of integration. We choose the Fields Programmable Gate Array (FPGA) hardware description language to implement the facial recognition algorithm based on "Eigen faces" using Principal Component Analysis. In this paper, we first present an overview of the PCA used for facial recognition,...
متن کاملComparison of Parameterization Methods in Recognizing Spoken Arabic Digits
This paper proposes evaluation of sound parameterization methods in recognizing some spoken Arabic words, namely digits from zero to nine. Each isolated spoken word is represented by a single template based on a specific recognition feature, and the recognition is based on the Euclidean distance from those templates. The performance analysis of recognition is based on four parameterization feat...
متن کامل