Most speech processing models begin with feature extraction and then pass the vector to primary model. The solution's performance mainly depends on quality of representation model architecture. Much research focuses designing robust deep network architecture ignoring representation's important role during neural era. This work aims exploit a new approach design signal in time-frequency domain v...

The most common mode of communication between humans is speech. As this is the most preferred way, humans would like to use speech to interact with machines also. That is why, automatic speech recognition has gained a lot of popularity. Many approaches for speech recognition exist like Dynamic Time Warping (DTW), Hidden Markov Model (HMM). This paper shows how Neural Network (NN) can be used fo...

Digital Speech Signal Processing is the process of converting one type of speech signal representation to another type of representation so as to uncover various mathematical or practical properties of the speech signal and do appropriate processing to support in solving both fundamental and deep troubles of interest. Digital Speech Processing chain has two different main model They are Speech ...

Automatic speech recognition systems rely on feature extraction techniques to improve their performance. Static features obtained from each frame are usually enhanced with dynamical components using derivative operations (delta features). However, the susceptibility to noise of the derivative impacts on the accuracy of the recognition in noisy environments. We propose an alternative to the delt...

The Ensemble Interval Histogram (EIH) is an auditory model which can be used as a robust \front-end" for speech recognition systems. The utilization of multiple level-crossing detectors in the EIH provides frequency and intensity information, which may be useful for speech processing. Proper determination of the number of levels and the level values is very important for reliable performance of...

Speech and language are considered uniquely human abilities Speech is a complex signal that is characterized by varying distributions of energy in time as well as in frequency, depending on the specific sound that is being produced. The aim of digital speech processing is to take advantage of digital computing techniques to process the speech signal for increased understanding, improved communi...

This paper introduces extended weighted linear prediction (XLP) to noise robust short-time spectrum analysis in the feature extraction process of a speech recognition system. XLP is a generalization of standard linear prediction (LP) and temporally weighted linear prediction (WLP) which have already been applied to noise robust speech recognition with good results. With XLP, higher controllabil...

Mild Cognitive Impairment (MCI), sometimes regarded as a prodromal stage of Alzheimer’s disease, is a mental disorder that is difficult to diagnose. However, recent studies reported that MCI causes slight changes in the speech of the patient. Our starting point here is a study that found acoustic correlates of MCI, but extracted the proposed features manually. Here, we automate the extraction o...

This year, the Computer Vision Group at University of Central Florida participated in two tasks in TRECVID 2004: High-Level Feature Extraction and Story Segmentation. For feature extraction task, we have developed the detection methods for “Madeleine Albright”, “Bill Clinton”, “Beach”, “Basketball Scored” and “People Walking/Running”. We used the adaboost technique, and has employed the speech ...