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This document summarizes the key components of a voice recognition system, including signal modeling and pattern matching. Signal modeling represents converting speech signals into parameters through operations like spectral shaping and feature extraction. Feature extraction analyzes speech signals through temporal and spectral analysis techniques to obtain parameters like power, pitch, and vocal tract configuration. Pattern matching finds the parameter set from memory that most closely matches the input speech parameters. The document then discusses specific temporal analysis techniques like power and energy analysis, and spectral analysis techniques like filter banks, cepstral analysis, and linear predictive coding analysis used for feature extraction in voice recognition systems.
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- 1. VOICE RECOGNITION SYSTEM Shailendra Singh Tiwari SGSITS ENGINEERING COLLEGE Computer Science Engineering INDORE (M.P.) singhtiwari.shailendra@gmail.com AB-24108 Introduction Abstract R.N.:O801CS123D13 Speech recognition system performs two fundamental operations: signal modeling and Automatic speech recognition (ASR) has made SGSITS pattern matching Signal modeling represents great strides with the development of digital process of converting speech signal into a set of signal processing hardware and software. But parameters. Pattern matching is the task of finding despite of all these advances, machines cannot parameter set from memory which closely match the performance of their human matches the parameter set obtained from the counterparts in terms of accuracy and speed, input speech signal. specially in case of speaker independent speech recognition. So today significant portion of Signal Modeling speech recognition research is focussed on speaker independent speech recognition problem. To obtain the perceptually meaningful parameters The reasons are its wide range of applications, i.e. parameters which are analogous to those used and limitations of available techniques of speech behuman auditory system. To obtain the invariant recognition. In this report we briefly discuss the parameters i.e. parameters which are robust to signal modeling approach for speech recognition. variations in channel, speaker and transducer. To It is followed by overview of basic operations obtain parameters that capture spectral dynamics, involved in signal modeling. Further commonly or changes of spectrum with time. The signal used temporal and spectral analysis techniques of modeling involves basic operationsSpectral feature extraction are discussed in detail. shapin spectral shapingis the process of converting the Analysis techniques for feature extraction have speech signal from sound pressure wave to a digital been studied in detail and following conclusions signal; and emphasizing important frequency are drawn components in the signal. Temporal analysis techniques involve less Feature extraction computation, ease of implementation. But they are limited to determination simple speech Feature extraction is process of obtaining different parameters like power, energy and periodicity of features such as power, pitch, and vocal tract speech. For finding vocal tract parameters we configuration from the speech signal. Parameter require spectral analysis techniques. Critical transformation is the process of converting these features into signal parameters through band filter bank decomposes the speech signal process of differentiation and concatenation. into discrete set of spectral samples containing Statistical modeling involves conversion of information, which is similar to information, parameters in signal observation vectors. presented to higher levels processing in auditory system. Cepstral analysis separates the speech Parametric transformation signal into component representing excitation source and a component representing vocal tract impulse response. Feature Extraction So it provides information about pitch and In speaker independent speech recogniton, a vocal tract configuration. But it is computationally premium is placed on extracting features that more intensive. Mel cepstral analysis has are somewhat invariant to changes in the speaker. decorrelating property of cepstral analysis and So feture extraction involves analysis of speech also includes some aspects of audition. LPC siganl. Broadly the feature extraction techniques analysis provides compact representation of vocal are classified as temporal analysis and spectral tract configuration by relatively simple analysis technique. In temporal analysis the computation compared tocepstral analysis. To speech waveform itself is used for analysis. In minimize analysis complexity it assumes all spectral analysis spectral representation of speech pole model for speech production system. But signal is used for analysis. speech has zeros due to nasals so in these cases the result are not as good as in case of vowels but still Conclusions reasonably acceptable if order of model is sufficiently high. The basic operations in speech recognition system have been discussed briefly. Different temporal and spectral
- 2. Acknowlegdement References I wish to express my sincere gratitude to Prof. L. R. Rabiner and R. W. Schafer, Digital puja gupta for her constant guidance throughout Processing of Speech Signals. Englewood Cliffs, the course of the computer workshop and many New Jersey: useful discussions which enabled me to know the subtleties of the subject in proper way. Prentice-Hall, 1978. D.O. Shaughnessy, Speech Communication: Human and Machine. India:University Press ,2001.