Comparative Study of Acoustic Echo Cancellation Algorithms for Speech Recognition System in Noisy Environment

Traditionally, adaptive filters have been deployed to achieve AEC by estimating the acoustic echo response using algorithms such as the Normalized Least-Mean-Square (NLMS) algorithm. Several approaches have been proposed over recent years to improve the performance of the standard NLMS algorithm in various ways for AEC. These include algorithms based on Time Domain, Frequency Domain, Fourier Transform, Wavelet Transform Adaptive Schemes, Proportionate Schemes, Proportionate Adaptive Filters, Combination Schemes, Block Based Combination, Sub band Adaptive Filtering, Uniform Over Sampled DFT Filter Banks, Sub band Over-Sampled DFT Filter Banks, Volterra Filters, Variable Step-Size (VSS) algorithms, Data Reusing Techniques, Partial Update Adaptive Filtering Techniques and Sub band (SAF) Schemes. These approaches aim to address issues in echo cancellation including the performance with noisy input signals, Time-Varying echo paths and computational complexity. In contrast to these approaches, Sparse Adaptive algorithms have been developed specifically to address the performance of adaptive filters in sparse system identification. In this paper we have discussed some AEC algorithms followed by comparative study with respective to step-size, convergence and performance.

Human Mood Detection For Human Computer Interaction

In this paper we propose an easiest approach for facial expression recognition. Here we are using concept of SVM for Expression Classification. Main problem is sub divided in three main modules. First one is Face detection in which we are using skin filter and Face segmentation. We are given more stress on feature Extraction. This method is effective enough for application where fast execution is required. Second, Facial Feature Extraction which is essential part for expression recognition. In this module we used Edge Projection Analysis. Finally extracted features vector is passed towards SVM classifier for Expression Recognition. We are considering six basic Expressions (Anger, Fear, Disgust, Joy, Sadness, and Surprise)

Image Optimization and Prediction

Image Processing, Optimization and Prediction of an Image play a key role in Computer Science. Image processing provides a way to analyze and identify an image .Many areas like medical image processing, Satellite images, natural images and artificial images requires lots of analysis and research on optimization. In Image Optimization and Prediction we are combining the features of Query Optimization, Image Processing and Prediction . Image optimization is used in Pattern analysis, object recognition, in medical Image processing to predict the type of diseases, in satellite images for predicting weather forecast, availability of water or mineral etc. Image Processing, Optimization and analysis is a wide open area for research .Lots of research has been conducted in the area of Image analysis and many techniques are available for image analysis but, a single technique is not yet identified for image analysis and prediction .our research is focused on identifying a global technique for image analysis and Prediction.

Performance Enhancement of Distributed Quasi Steady-State Genetic Algorithm

This paper proposes a new scheme for performance enhancement of distributed genetic algorithm (DGA). Initial population is divided in two classes i.e. female and male. Simple distance based clustering is used for cluster formation around females. For reclustering self-adaptive K-means is used, which produces well distributed and well separated clusters. The self-adaptive K-means used for reclustering automatically locates initial position of centroids and number of clusters. Four plans of co-evolution are applied on these clusters independently. Clusters evolve separately. Merging of clusters takes place depending on their performance. For experimentation unimodal and multimodal test functions have been used. Test result show that the new scheme of distribution of population has given better performance.

Automatic Speech Recognition Using Template Model for Man-Machine Interface

Speech is a natural form of communication for human beings, and computers with the ability to understand speech and speak with a human voice are expected to contribute to the development of more natural man-machine interfaces. Computers with this kind of ability are gradually becoming a reality, through the evolution of speech recognition technologies. Speech is being an important mode of interaction with computers. In this paper Feature extraction is implemented using well-known Mel-Frequency Cepstral Coefficients (MFCC).Pattern matching is done using Dynamic time warping (DTW) algorithm.

An Overview of Hindi Speech Recognition

In this age of information technology, information access in a convenient manner has gained importance. Since speech is a primary mode of communication among human beings, it is natural for people to expect to be able to carry out spoken dialogue with computer. Speech recognition system permits ordinary people to speak to the computer to retrieve information. It is desirable to have a human computer dialogue in local language. Hindi being the most widely spoken Language in India is the natural primary human language candidate for human machine interaction. There are five pairs of vowels in Hindi languages; one member is longer than the other one. This paper describes an overview of speech recognition system that includes how speech is produced and the properties and characteristics of Hindi Phoneme.

Opportunities & Challenges In Automatic Speech Recognition

Automatic speech recognition enables a wide range of current and emerging applications such as automatic transcription, multimedia content analysis, and natural human-computer interfaces. This paper provides a glimpse of the opportunities and challenges that parallelism provides for automatic speech recognition and related application research from the point of view of speech researchers. The increasing parallelism in computing platforms opens three major possibilities for speech recognition systems: improving recognition accuracy in non-ideal, everyday noisy environments; increasing recognition throughput in batch processing of speech data; and reducing recognition latency in realtime usage scenarios. This paper describes technical challenges, approaches taken, and possible directions for future research to guide the design of efficient parallel software and hardware infrastructures.

Speech Enhancement Using Pitch Detection Approach For Noisy Environment

Acoustical mismatch among training and testing phases degrades outstandingly speech recognition results. This problem has limited the development of real-world nonspecific applications, as testing conditions are highly variant or even unpredictable during the training process. Therefore the background noise has to be removed from the noisy speech signal to increase the signal intelligibility and to reduce the listener fatigue. Enhancement techniques applied, as pre-processing stages; to the systems remarkably improve recognition results. In this paper, a novel approach is used to enhance the perceived quality of the speech signal when the additive noise cannot be directly controlled. Instead of controlling the background noise, we propose to reinforce the speech signal so that it can be heard more clearly in noisy environments. The subjective evaluation shows that the proposed method improves perceptual quality of speech in various noisy environments. As in some cases speaking may be more convenient than typing, even for rapid typists: many mathematical symbols are missing from the keyboard but can be easily spoken and recognized. Therefore, the proposed system can be used in an application designed for mathematical symbol recognition (especially symbols not available on the keyboard) in schools.

Speech: A Challenge to Digital Signal Processing Technology for Human-to-Computer Interaction

This software project based paper is for a vision of the near future in which computer interaction is characterized by natural face-to-face conversations with lifelike characters that speak, emote, and gesture. The first step is speech. The dream of a true virtual reality, a complete human-computer interaction system will not come true unless we try to give some perception to machine and make it perceive the outside world as humans communicate with each other. This software project is under development for listening and replying machine (Computer) through speech. The Speech interface is developed to convert speech input into some parametric form (Speech-to-Text) for further processing and the results, text output to speech synthesis (Text-to-Speech)

Speech Enhancement Modeling Towards Robust Speech Recognition System

Form about four decades human beings have been dreaming of an intelligent machine which can master the natural speech. In its simplest form, this machine should consist of two subsystems, namely automatic speech recognition (ASR) and speech understanding (SU). The goal of ASR is to transcribe natural speech while SU is to understand the meaning of the transcription. Recognizing and understanding a spoken sentence is obviously a knowledge-intensive process, which must take into account all variable information about the speech communication process, from acoustics to semantics and pragmatics. While developing an Automatic Speech Recognition System, it is observed that some adverse conditions degrade the performance of the Speech Recognition System. In this contribution, speech enhancement system is introduced for enhancing speech signals corrupted by additive noise and improving the performance of Automatic Speech Recognizers in noisy conditions. Automatic speech recognition experiments show that replacing noisy speech signals by the corresponding enhanced speech signals leads to an improvement in the recognition accuracies. The amount of improvement varies with the type of the corrupting noise.