Feature extraction plays a substantial role in automatic speech recognition systems. In this paper, a method is proposed to extract time-varying acoustic features that are effective for speech recognition. This issue is discussed from two aspects: one is on speech power spectrum enhancement and the other on discriminative time-varying feature extraction which employs subphonetic units, called d...

Detection of acoustic phonetic landmarks is useful for a variety of speech processing applications such as automatic speech recognition.The majority of existing methods use Melfrequency Cepstral Coefficients (MFCCs) describing the short time power spectral envelope of the speech signal. This paper hypothesizes that a different feature extraction method can be used to complement MFCCs by capturi...

− Audio-Visual Automatic Speech Recognition systems use visual information to enhance ASR systems in clean and noisy environments. This paper compares of a number of different visual feature extraction methods. When performing visual speech recognition the visual feature vector requires a base level of detail for optimum recognition. Geometric feature extraction provides lower recognition than ...

Within an Audio-Visual Speech Recognition (AVSR) framework an important process is video feature extraction. Several methods are available, but all of them require mouth region extraction. To achieve this, a semi-automatic system based on nostril detection is presented. The system is designed to work on ordinary frontal videos and to be able to recover brief nostril occlusion. Using the nostril...

A compact representation of speech is possible using Bessel functions because of the similarity between voiced speech and the Bessel functions. Both voiced speech and the Bessel functions exhibit quasiperiodicity and decaying amplitude with time. In this paper, we have developed various feature extraction techniques using zero-order Bessel functions as basis functions for the task of closed-set...

-This paper addresses the issue of speaker identification in overlapping speech where two speakers are speaking simultaneously over a single communication channel where objective is to find individual speaker identities. This task can be accomplished using feature extraction techniquesbased on which classification model can be developed.This paper studies different feature extraction techniques...

Speech has evolved as a primary form of communication between humans. The advent of digital technology, gave us highly versatile digital processors with high speed, low cost and high power which enable researchers to transform the analog speech signals in to digital speech signals that can be scientifically studied. Achieving higher recognition accuracy, low word error rate and addressing the i...

We investigated a robust speech feature extraction method using kernel PCA (Principal Component Analysis) for distorted speech recognition. Kernel PCA has been suggested for various image processing tasks requiring an image model, such as denoising, where a noise-free image is constructed from a noisy input image [1]. Much research for robust speech feature extraction has been done, but it rema...

This paper addresses robust speech feature extraction in combination with statistical speech feature enhancement and couples the particle filter to the speech recognition hypotheses. To extract noise robust features the Fourier transformation is replaced by the warped and scaled minimum variance distortionless response spectral envelope. To enhance the features, particle filtering has been used...

Speech is a time varying signal. What makes it more interesting is that the information contained in the signal is very difficult to analyze. Traditional methods of speech analysis use Short Time Fourier Transform and Mel Frequency Cepstral Coefficients (MFCC) to extract the feature out of a speech signal, and the model has been successfully implemented in many speech recognition machines. Thei...