Kurukshetra University, Department of Instrumentation & Control Engineering., H.E.C* Jagadhri, Haryana, 135003, India sachdevaanjali26@gmail.com ABHIJEET KUMAR Mullana University, Department of Electronics and Comm. Engineering., M.M.E.C Mullana, Haryana, 133203, India abhijeetsliet@gmail.com NIDHIKA BIRLA Kurukshetra University, Department of Electronics Engineering., H.E.C Jagadhri, Haryana, 135003, India nidhikabirla@gmail.com Abstract: The Voice is a signal of infinite information. Digital processing of speech signal is very important for high-speed and precise automatic voice recognition technology. Nowadays it is being used for health care, telephony military and people with disabilities therefore the digital signal processes such as Feature Extraction and Feature Matching are the latest issues for study of voice signal. In order to extract valuable information from the speech signal, make decisions on the process, and obtain results, the data needs to be manipulated and analyzed. Basic method used for extracting the features of the voice signal is to find the Mel frequency cepstral coefficients. Mel-frequency cepstral coefficients (MFCCs) are the coefficients that collectively represent the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency.This paper is divided into two modules. Under the first module feature of the speech signal are extracted in the form of MFCC coefficients and in another module the non linear sequence alignment known as Dynamic Time Warping (DTW) introduced by Sakoe Chiba has been used as features matching techniques. Since it’s obvious that the voice signal tends to have different temporal rate, the alignment is important to produce the better performance. This paper presents the feasibility of MFCC to extract features and DTW to compare the test patterns.

Voice command recognition system based on mfcc and dtw

Although wavelet analysis has been proposed for speech processing as an alternative to Fourier analysis, most approaches make use of off-the-shelf wavelets and dyadic tree-structured filter banks. In this paper, we extend previous wavelet-based frameworks in two ways. First, we increase the flexibility in wavelet selection by taking advantage of the relationship between wavelets and filter banks and by designing new wavelets using filter design methods. We adopt two filter design techniques that we refer to as filter matching and attenuation minimization. Second, we improve the flexibility in frequency partitioning by implementing rational as well as dyadic filter banks. Rational filter banks naturally incorporate the critical-band effect in the human auditory system. To test our extensions, we implement an energy-based measurement which we also compare in performance to the mel-frequency cepstral coefficients (MFCCs) in a phonetic classification task. We show that the designed wavelets outperform off-the-shelf wavelets as well as an MFCC baseline

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An Implementation of Rational Wavelets and Filter Design for Phonetic Classification

Discriminant neural predictive coding applied to phoneme recognition

We present a wavelet and filter bank framework for context-independent phonetic classification with the aim of extending the work towards a full speech recognition system. The framework addresses the limitations of the Fourier analysis stage commonly used for short-time spectral representation of speech signals. Also, previous research pertaining to wavelet analysis for speech processing mostly makes use of off-the-shelf wavelets and dyadic-based signal decomposition. Our framework provides more flexibility by taking advantage of the relationship between wavelet transforms and filter banks, and using two filter design techniques as well as 'rational' wavelets. On the standard 39 phone TIMIT classification task, we achieve 22.9% error rate on the core test set using rational filter banks and 4-fold aggregation. This is improved to 18.5% when combined with multiple classifiers defined over non-wavelet acoustic measurements.

/pdf/a-wavelet-and-filter-bank-framework-for-phonetic-44vd0p8sjz.pdf

A wavelet and filter bank framework for phonetic classification

Voice Recognition is often confused with Voice and speech Recognition, which is the translation of spoken words (voice and speech) into text. But the identification of "who" is speaking is not the same activity as the recognition of what words are being spoken. The main objective of this research is to develop a system for voice recognition in Tamil Word and Numeral using Mel-Frequency Cepstral Coefficients (MFCC) and Dynamic Time Warping (DTW). Tamil language has 247 letters, but most of them are derived from the 12 vowels and 18 consonants. The other 216 letters are made by combining the sounds of a vowel and a consonant. Each letter is having unique sound. Voice recognition is the system by which sounds, words or phrases spoken by humans are converted into electrical signals and these signals are transformed into coding patterns to which meaning has been assigned. To extract valuable information from the voice and speech signal, make decisions on the process, and obtain results, the data needs to be manipulated and analyzed. This research work presents the feasibility of MFCC to extract features and DTW to compare the Tamil words and numerals test patterns. The extraction and matching Process is implemented right after the Pre Processing or filtering signal is performed.

Voice and Speech Recognition for Tamil Words and Numerals

The goal is to improve recognition rate by optimisation of Mel Frequency Cepstral Coefficients (MFCCs): modifications concern the time-frequency representation used to estimate these coefficients. There are many ways to obtain a spectrum out of a signal which differ in the method itself (Fourier, Wavelets,…), and in the normalisation. We show here that we can obtain noise resistant cepstral coefficients, for speaker independent connected word recognition. The recognition system is based on a continuous whole word hidden Markov model. An error reduction rate of approximately 50% is achieved. Moreover evaluation tests demonstrate that these results can be obtained with smaller databases: halving the training database have small effects on recognition rates (which is not the case with traditional MFCCs).

http://publications.idiap.ch/downloads/reports/1996/eusipco96.pdf

Hubert Wassner

Papers

New time-frequency derived cepstral coefficients for automatic speech recognition