Improvement of Text Dependent Speaker Identification System Using Neuro-Genetic Hybrid Algorithm in Office Environmental Conditions

In this paper, an improved strategy for automated text dependent speaker identification system has been proposed in noisy environment. The identification process incorporates the Neuro-Genetic hybrid algorithm with cepstral based features. To remove the background noise from the source utterance, wiener filter has been used. Different speech pre-processing techniques such as start-end point detection algorithm, pre-emphasis filtering, frame blocking and windowing have been used to process the speech utterances. RCC, MFCC, ?MFCC, ??MFCC, LPC and LPCC have been used to extract the features. After feature extraction of the speech, Neuro-Genetic hybrid algorithm has been used in the learning and identification purposes. Features are extracted by using different techniques to optimize the performance of the identification. According to the VALID speech database, the highest speaker identification rate of 100.000% for studio environment and 82.33% for office environmental conditions have been achieved in the close set text dependent speaker identification system

On preprocessing of speech signals

Preprocessing of speech signals is considered a crucial step in the development of a robust and efficient speech or speaker recognition system. In this paper, we present some popular statistical outlier-detection based strategies to segregate the silence/unvoiced part of the speech signal from the voiced portion. The proposed methods are based on the utilization of the 3 σ edit rule, and the Hampel Identifier which are compared with the conventional techniques: (i) short-time energy (STE) based methods, and (ii) distribution based methods. The results obtained after applying the proposed strategies on some test voice signals are encouragin

Suppression of acoustic noise in speech using spectral subtraction

technical reportA stand alone noise suppression algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital waveform. Spectral subtraction offers a computationally efficient, processor independent, approach to effective digital speech analysis. The method, requiring about the same computation as high-speed convolution, suppresses stationary noise for speech by subtracting the spectral noise bias calculated during non-speech activity. Secondary procedures and then applied to attenuate the residual noise left after subtraction. Since the algorithm resynthesizes a speech waveform, it can be used as a preprocessor to narrow band voice communications systems, speech recognition systems or speaker authentication systems

Acoustic analysis of Sindhi speech - a pre-curser for an ASR system

The functional and formative properties of speech sounds are usually referred to as acoustic-phonetics in linguistics. This research aims to demonstrate acoustic-phonetic features of the elemental sounds of Sindhi, which is a branch of the Indo-European family of languages mainly spoken in the Sindh province of Pakistan and in some parts of India. In addition to the available articulatory-phonetic knowledge; acoustic-phonetic knowledge has been classified for the identification and classification of Sindhi language sounds. Determining the acoustic features of the language sounds helps to bring together the sounds with similar acoustic characteristics under the name of one natural class of meaningful phonemes. The obtained acoustic features and corresponding statistical results for a particular natural class of phonemes provides a clear understanding of the meaningful phonemes of Sindhi and it also helps to eliminate redundant sounds present in the inventory. At present Sindhi includes nine redundant, three interchanging, three substituting, and three confused pairs of consonant sounds. Some of the unique acoustic-phonetic features of Sindhi highlighted in this study are determining the acoustic features of the large number of the contrastive voiced implosives of Sindhi and the acoustic impact of the language flexibility in terms of the insertion and digestion of the short vowels in the utterance. In addition to this the issue of the presence of the affricate class of sounds and the diphthongs in Sindhi is addressed. The compilation of the meaningful language phoneme set by learning their acoustic-phonetic features serves one of the major goals of this study; because twelve such sounds of Sindhi are studied that are not yet part of the language alphabet. The main acoustic features learned for the phonological structures of Sindhi are the fundamental frequency, formants, and the duration — along with the analysis of the obtained acoustic waveforms, the formant tracks and the computer generated spectrograms. The impetus for doing such research comes from the fact that detailed knowledge of the sound characteristics of the language-elements has a broad variety of applications — from developing accurate synthetic speech production systems to modeling robust speaker-independent speech recognizers. The major research achievements and contributions this study provides in the field include the compilation and classification of the elemental sounds of Sindhi. Comprehensive measurement of the acoustic features of the language sounds; suitable to be incorporated into the design of a Sindhi ASR system. Understanding of the dialect specific acoustic variation of the elemental sounds of Sindhi. A speech database comprising the voice samples of the native Sindhi speakers. Identification of the language‘s redundant, substituting and interchanging pairs of sounds. Identification of the language‘s sounds that can potentially lead to the segmentation and recognition errors for a Sindhi ASR system design. The research achievements of this study create the fundamental building blocks for future work to design a state-of-the-art prototype, which is: gender and environment independent, continuous and conversational ASR system for Sindhi

A Novel Approach for Continuous Speech Tracking and Dynamic Time Warping. Adaptive Framing Based Continuous Speech Similarity Measure and Dynamic Time Warping using Kalman Filter and Dynamic State Model

Dynamic speech properties such as time warping, silence removal and background noise interference are the most challenging issues in continuous speech signal matching. Among all of them, the time warped speech signal matching is of great interest and has been a tough challenge for the researchers. An adaptive framing based continuous speech tracking and similarity measurement approach is introduced in this work following a comprehensive research conducted in the diverse areas of speech processing. A dynamic state model is introduced based on system of linear motion equations which models the input (test) speech signal frame as a unidirectional moving object along the template speech signal. The most similar corresponding frame position in the template speech is estimated which is fused with a feature based similarity observation and the noise variances using a Kalman filter. The Kalman filter provides the final estimated frame position in the template speech at current time which is further used for prediction of a new frame size for the next step. In addition, a keyword spotting approach is proposed by introducing wavelet decomposition based dynamic noise filter and combination of beliefs. The Dempster’s theory of belief combination is deployed for the first time in relation to keyword spotting task. Performances for both; speech tracking and keyword spotting approaches are evaluated using the statistical metrics and gold standards for the binary classification. Experimental results proved the superiority of the proposed approaches over the existing methods.The appendices files are not available online