Mel-scaled discrete wavelet coefficients for speech recognition

Gowdy, J.N.; Tüfekçi, Zekeriya

doi:10.1109/icassp.2000.861829

Cited by 80 publications

(33 citation statements)

References 11 publications

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“…The binary wavelet used commonly divides frequency band in octave band way (Gowdy et al, 2000). This does not fit entirely to the auditory character of human ear to speech.…”

Section: The Principle Of Bark Waveletmentioning

confidence: 99%

The Research of Noise-Robust Speech Recognition Based on Frequency Warping Wavelet

Zhang¹,

Wang²

2007

Robust Speech Recognition and Understanding

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“…The binary wavelet used commonly divides frequency band in octave band way (Gowdy et al, 2000). This does not fit entirely to the auditory character of human ear to speech.…”

Section: The Principle Of Bark Waveletmentioning

confidence: 99%

The Research of Noise-Robust Speech Recognition Based on Frequency Warping Wavelet

Zhang¹,

Wang²

2007

Robust Speech Recognition and Understanding

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“…Artificial neural networks (ANN) are known to be excellent classifiers, but their performance can be prevented by the size and quality of the training set. Fuzzy theory has been used successfully in many applications (Gowdy & Tufekci, 2000). This applications show that fuzzy theory can be used to improve neural network performance.…”

Section: Introductionmentioning

confidence: 99%

Discrete Wavelet Transform & Linear Prediction Coding Based Method for Speech Recognition via Neural Network

Daqrouq¹,

Al‐Qawasmi²,

Azzawi³

et al. 2011

Discrete Wavelet Transforms - Biomedical Applications

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“…However usage of MFCC has several issues and the experimental results showed that this technique does not work well in noisy speech environment [3]. Also to extract features, speech signal is divided into fixed size of frames and in these static size frames sharp transition that occur in signal cannot be analyzed [4,5]. Map (SOM) is used as an input to be fed into the ensemble classifier [6].…”

Section: Introductionmentioning

confidence: 99%

Speech Query Recognition for Tamil Language Using Wavelet and Wavelet Packets

Iswarya¹,

Radha²

2015

J Inf Process Syst

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Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.

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Mel-scaled discrete wavelet coefficients for speech recognition

Cited by 80 publications

References 11 publications

The Research of Noise-Robust Speech Recognition Based on Frequency Warping Wavelet

The Research of Noise-Robust Speech Recognition Based on Frequency Warping Wavelet

Discrete Wavelet Transform & Linear Prediction Coding Based Method for Speech Recognition via Neural Network

Speech Query Recognition for Tamil Language Using Wavelet and Wavelet Packets

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