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In this paper, we update our previous research for Mel-Frequency Cepstral Coefficient (MFCC) feature extraction [1] and describe the optimizations required for improving throughput on the Graphics Processing Units (GPU). We not only demonstrate that the feature extraction process is suitable for GPUs and a substantial reduction in computation time can be obtained by performing feature extraction on...
In this paper, we present an efficient parallel implementation of Mel-frequency Cepstral Coefficient (MFCC)-based feature extraction and describe the optimizations required for effective throughput on Graphics Processing Units (GPU) processors. We demonstrate that the feature extraction process in automatic speech recognition is well suited for GPUs and a substantial reduction in computation time...
In this paper, a new approach in human identification is investigated. For this purpose, we fused Dorsal Hand Vein and ECG biometrics to achieve a multimodal biometric system. In the proposed system for fusing biometrics, we used Mel Frequency Coefficient Cepstrum (MFCC) approach in order to extract features of ECG biometric and Line Segment Hausdorff Distance method (LsHD) for matching the vein structures...
Continuous, real-time speech recognition is required for various mobile and hands-free applications. In this paper, hardware implementation of real-time speech recognition system is proposed using two approaches and their performances are evaluated. The first approach uses Mel Filter Banks with Mel Frequency Cepstrum Coefficients (MFCC) as feature input and the second approach uses Cochlear Filter...
In this paper a feature extraction technique using Reconstructed Phase Spaces (RPS) is presented, which improves the overall performances of typical speech recognition systems. Unlike conventional feature extraction methods that use FFT based algorithm as power spectrum estimation (PSE) of speech signal, the proposed method is based on the trajectory and flow matrix of signal's RPS. In this manner,...
Mel-frequency cepstrum coefficient (MFCC) is a widely used feature vector in speech signal precessing. Its feature extraction procedure can be seen as a mapping function which transfers the input speech signals to output MFCC feature vectors. However, this function is too complex to analyze and even a simple approximation is not easy to obtain. This paper studies the effects of each MFCC feature extraction...
This paper studies the feasibility of information analysis processing technology, which fuses speech and image together in the real-time monitoring system. It emphasizes particularly on speech analysis and fuses these two technologies in terms of scoring strategy. It also makes some improvement on MFCC feature extraction and proposes a quick MFCC algorithm. The proposed algorithm can reach the requirement...
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