Bharti W. Gawali

A Review on Speech Recognition Technique

The Speech is most prominent & primary mode of Communication among of human being. The communication among human computer interaction is called human computer interface. Speech has potential of being important mode of interaction with computer .This paper gives an overview of major technological perspective and appreciation of the fundamental progress of speech recognition and also gives overview technique developed in each stage of speech recognition. This paper helps in choosing the technique along with their relative merits & demerits. A comparative study of different technique is done as per stages. This paper is concludes with the decision on feature direction for developing technique in human computer interface system using Marathi Language.

Performance Evaluation of Speech Synthesis Techniques for English Language

The conversion of text to synthetic production of speech is known as text-to-speech synthesis (TTS). This can be achieved by the method of concatenative speech synthesis (CSS) and hidden Markov model techniques. Quality is the important paradigm for the artificial speech produced. The study involves the comparative analysis for quality of speech synthesis using hidden Markov model and unit selection approach. The quality of synthesized speech is evaluated with the two methods, i.e., subjective measurement using mean opinion score and objective measurement based on mean square score and peak signal-to-noise ratio (PSNR). Mel-frequency cepstral coefficient features are also extracted for synthesized speech. The experimental analysis shows that unit selection method results in better synthesized voice than hidden Markov model.

Feature extraction using fusion MFCC for continuous marathi speech recognition

This paper presents the performance of feature extraction techniques for speech recognition, for the classification of speech represented by a particular continuous sentence model. The goal of this study is to present independent as well as comparative performances of popular appearance based feature extraction techniques i.e. Linear Discriminative Analysed and Mel Frequency Cestrum Coefficient. Mel Frequency Cepstrum Coefficient (MFCC) helps us in extracting feature where as linear discriminant analysis (LDA) is used for reducing dimension of extracted feature. We experimented MFCC feature extraction individually and proposed a Fusion of MCCC and LDA for feature extraction.

Comparative Analysis of MSER and DTW for Offline Signature Recognition

This paper describes the signature as a one of the most robust and widely used trait in biometric verification. The signature is also known as behavioral biometric property. We present comparative analysis of offline signature recognition based on Dynamic Time Warping (DTW) and Maximally Stable Extremely Regions (MSER). The MSER system reported accuracy of 95.371% where FAR is 5.25% and FRR is 4% whereas DTW system reported accuracy of 70.5% and average horizontal projection WER 70% and average vertical projection WER of 71%.

Accent Recognition for Indian English using Acoustic Feature Approach

Accent is the basic pattern of acoustic feature and pronunciation. It can identify the person’s social and linguistic background. It is an important source of inter as well as intra speaker variability. The accent dependent dictionary or model can be used to improve accuracy of speech recognition system. In this study we present an experimental approach of acoustic speech feature for Marathi & Arabic accents for English speaking. The detail study of acoustics correlates the accent using formant frequency, energy and pitch characteristics. The database consists of speech from speaker with Marathi as their mother tongue and speakers from Iraq with Arabic language as mother tongue. Both the speakers were asked to speak English number from zero to nine. Through experimental results the fifth formant frequency found to be very effective for accent recognition.

Novel approach based feature extraction for Marathi continuous speech recognition

This paper examines the performance of feature extraction techniques and the classification of speech represented by continuous Marathi speech model. The goal of this study is to present independent as well as comparative performances of most popular appearance based feature extraction techniques i.e. Principal Component Analysis, Linear Discriminative Analysis, Mel Frequency Cepstrum Coefficient and Discrete Wavelet Transformation. To improve performance of speech recognition, it needed to perform dimensionality reduction of speech data. The Motivation was the lack of direct and detailed independent comparison in all possible algorithms. Mel Frequency Cepstrum Coefficient (MFCC) help us in extracting feature where as Principal Component Analysis (PCA), linear discriminate analysis (LDA), and Discrete Wavelet Transformation are used for reducing dimension of extracted feature. We experimented with several combinations by combining techniques such as MFCC+PCA (MFPCA), MFCC+LDA (MFLDA), MFCC+DWT (MFDWT), MFCC+PCA+DWT (MFPDWT) and MFCC+LDA+ DWT (MFLDWT).The performance of technique is compared on accuracy as well as real time factor. The MFCC+LDA+DWT (MFLDWT) is a best combination which provides the accuracy of 95.06% where real time factor is 0.35 second as compared to other combination.

Technical Aspects of Brain Rhythms and Speech Parameters

Speech is a basic communication mechanism in human beings. Its main intention is to convey a message, using a sequence of sound units of a language. Speech produces sound waves modulated by vocal tract systems, whereas the brain produces brain waves from electrical signals produced by billions of brain cells called neurons. This chapter covers details of both types of signals. It gives comprehensive information about brain rhythms, and techniques for processing brain and speech signals and extracting their features. The chapter focuses on signal processing algorithms related to both types of signals, which are categorized in three groups: preprocessing, feature extraction, and feature classification.

Creation of Marathi speech corpus for automatic speech recognition

This paper describes the collection of audio corpus for Marathi language. Marathi is one of the regional Indian languages. There are 12 vowels and 36 consonants present in Marathi languages. The objective of the research is to create the speech corpus which can be used for automatic speech recognition system for various domains like telephonic inquiry system, teaching tutor etc. The size of corpus collected is 28420 isolated words and 17470 sentences from around 500 speakers. The speech utterances were recorded in 16 kHz in three recording medium, a headset, desktop mounted microphone and Mobile phone. The corpus is transcripted as well as annotated and is available for recognition system.

Status of Wireless Technologies Used For Designing Home Automation System - A Review

The concept of “Automation” have just started flourishing, companies have developed automated systems of their own to control alarms, sensors, actuators and video cameras and moving further the concept of automated buildings is being recognized. This Paper attempts to study standards / technologies which are used for Home Automation. In brief, concern of this Paper is to cover the detail Technical aspects of the Home Automation Standard/ Technology.

Songs induced mood recognition system using EEG signals

Background Brain computer interfacing is a system that acquires and analyzes neural signals to create a communication channel directly between the brain and the computer. The EEG records the electrical fields generated by the nerve cells. With the help of Fourier Transformation the EEG signals are classified into four different frequency bands. Purpose The main purpose of the present paper is to report results related to classification of EEG signals of different people subjected to different conditions. Methods The experiment has been done on 10 subjects having activities related to hearing music chosen from categories of patriotic, happy, romantic and sad songs along with relaxation activity. 19 electrodes have been used under (10–20) International Standard. The δ, θ α and β components of EEG signals to these activities have been determined. Different statistical methods including linear discriminate analysis have been tested for classification. Results Result of the Linear Discriminant Analysis (LDA) made four groups of all modes (Relaxation, Happy, Sad, Patriotic and Romantic Song) labeled group1, Group2, Group3 and Group4 of all ten electrodes for Delta, Theta, alpha and Beta frequencies. Conclusion The study may be used for the development of activities induced mood recognition (AIMR) system from the EEG signal.