This paper proposes fusion and addition techniques of vocal tract features such as Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Mel Frequency Cepstral Coefficients (DMFCC) in speaker identification. Feature extraction plays an important role as a front end processing block in Speaker Identification (SI) process. Mel frequency features are used to extract the spectral characteristics o...

We propose a new scheme to reduce phase sensitivity in independent component analysis (ICA)-based feature extraction using an analytical description of the ICAadapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a spectral-domain ICA stage that removes redundant time shift information. The performance of the new scheme is evalu...

Fast Fourier Transform (FFT) plays an important role in the field of digital signal processing. High performance FFT processors are widely used in different application, such as speech processing, image processing, and communication system. In this paper, we proposed a novel register array based low power FFT processor for Mel Frequency Cepstral Coefficient (MFCC). Compared with [9-12], this no...

Automatic Speech Recognition (ASR) is a key component in Human Computer Interaction (HCI) applications. Stability of ASR systems largely depends on accent, gender, age of speakers, background noise and channel variations. In this paper, a study has been conducted to classify five different accents of Urdu language spoken in Pakistan i.e. Punjabi, Urdu, Pashto, Saraiki and Sindhi. Speech data ha...

The study performs feature extraction for isolated word recognition using Mel-Frequency Cepstral Coefficient (MFCC) for Gujarati language. It explains feature extraction methods MFCC and Linear Predictive Coding (LPC) in brief. The paper compares the performances of MFCC and LPC features under Vector Quantization (VQ) method. The dataset comprising of males and females voices were trained and t...

This study focuses on the detection of shouted speech in realistic noisy conditions. An automatic system based on modified mel frequency cepstral coefficient (MFCC) feature extraction and Gaussian mixture model (GMM) classification is developed. The performance of the automatic system is compared against human perception measured by a listening test. At moderate noise levels, the automatic syst...

This paper presents experimental results on whispered speech recognition based Teager Energy Operator for linear and mel cepstral coefficients including the Cepstral Mean Subtraction normalization technique. The feature vectors taken into consideration are Linear Frequency Coefficients, Mel Coefficients Coefficients. A speaker dependent scenario is used. For process, Dynamic Time Warping Hidden...

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...