Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques

By Lin Muda, Mumtaj Begam, Irraivan Elamvazuthi. 2010

1,143 citations recorded by the source corpus.

Abstract — Digital processing of speech signal and voice recognition algorithm is very important for fast and accurate automatic voice recognition technology. The voice is a signal of infinite information. A direct analysis and synthesizing the complex voice signal is due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching are introduced to represent the voice signal. Several methods such as Liner Predictive Predictive Coding (LPC), Hidden Markov Model (HMM), Artificial Neural Network (ANN) and etc are evaluated with a view to identify a straight forward and effective method for voice signal. The extraction and matching process is implemented right after the Pre Processing or filtering signal is performed. The non-parametric method for modelling the human auditory perception system, Mel Frequency Cepstral Coefficients (MFCCs) are utilize as extraction techniques. 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 present the viability of MFCC to extract features and DTW to compare the test patterns.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Continuous Authentication for Voice Assistants (2017, background): “The production of human speech is commonly modeled as the combined effect of two separate processes [16]: a voice source (vibration of vocal folds) that generates the original signal and a filter (determined by the resonant properties of vocal tract including the influence of tongue and lips) that…”
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition (2018, methodology): “Spearman’s rho is widely used to represent the correlation between two variables, and can be calculated as follows: r(X ,Y ) =Cov(X ,Y )/ √ Var[X ]Var[Y ], where X and Y are the MFCC features of the two pieces of audio.”
Effective and General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping (2019, background): “DTW is a similarity function for time series that has long been known and used in speech processing [12, 13, 14], robotics [15, 16], data mining [17, 18], handwriting recognition [19], gesture recognition [20, 21] and many other areas [22, 23, 24].”