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...

Abst rac t Experimental results of the quantization of Linear Predictive Coded (LPC) coefficients using two general approaches, scalar coefficient quantization and vector quantization, are presented. The LPC coefficients were quantized in several domains: Line Spectral Frequency (LSF), cepstral, predictor, reflection and autocorrelation. Two distortion measures were used to evaluate the quantiz...

Speech recognition is a method of finding similarity between two sequences. Various researches have been done on it. In our research, we are trying to achieve the optimal accuracy during the recognition procedure. Here, we are extracting features of the voice sample before filtering it through a noise reduction filter. For each individual, there are number of features are taken using feature ex...

This paper presents a brief survey on Automatic Voice Recognition so as to provide a technological perspective and an appreciation of the fundamental progress that has been accomplished in area of voice communication. The voice is a signal of infinite information. After years of research and development the accuracy of automatic voice recognition remains one of the important research challenges...

To improve the performance of Automatic Speech Recognition (ASR) Systems, a new method is proposed to extract features capable of operating at a very low signal-to-noise ratio (SNR). The basic idea introduced in this article is to enhance speech quality as the first stage for Mel-cepstra based recognition systems, since it is well-known that cepstral coefficients provided better performance in ...

Human Voice is characteristic for an individual. The ability to recognize the speaker by his/her voice can be a valuable biometric tool with enormous commercial as well as academic potential. Commercially, it can be utilized for ensuring secure access to any system. Academically, it can shed light on the speech processing abilities of the brain as well as speech mechanism. In fact, this feature...

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...

Development of robust and efficient front-end is crucial for robust ASR. Proper time and frequency resolution of the TFR of speech, motivated by the auditory models is considered an important factor for robustness. An efficient method of realizing a variable resolution TFR using DTFT/Goertzel algorithm is proposed instead of the standard FFT based approach. It is shown that the new representati...