Fast Dynamic Speech Recognition via Discrete Tchebichef Transform

Ernawan, Ferda; Noersasongko, Edi; Abu, Nur Azman

doi:10.1109/ici.2011.48

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…The Discrete Tchebichef Transform (DTT) is another transform method based on discrete Tchebichef polynomials. DTT has a lower computational complexity and it does not require complex transform unlike continuous orthonormal transforms (Ernawan et al, 2011a). At the same time, DTT does not involve any numerical approximation on a computationally friendly domain.…”

Section: Introductionmentioning

confidence: 99%

Smooth Formant Peak Via Discrete Tchebichef Transform

Abu¹,

Ernawan²,

Salim³

2015

Journal of Computer Science

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With the growth in computing power, speech recognition carries a strong potential in the near future. It has even become increasingly popular with the development of mobile devices. Presumably, mobile devices have limited computational power, memory size and battery life. In general, speech recognition operation requires heavy computation due to large samples per window used. Fast Fourier Transfom (FFT) is the most popular transform to search for formant frequencies in speech recognition. In addition, FFT operates in complex fields with imaginary numbers. This paper proposes an approach based on Discrete Tchebichef Transform (DTT) as a possible alternative to FFT in searching for the formant frequencies. The experimental outputs in terms of the frequency formants using FFT and DTT have been compared. Interestingly, the experimental results show that both have produced relatively identical formant shape output in terms of basic vowels and consonants recognition. DTT has the same capability to recognize speech formants F 1 , F 2 , F 3 on real domains.

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Section: Introductionmentioning

confidence: 99%

Smooth Formant Peak Via Discrete Tchebichef Transform

Abu¹,

Ernawan²,

Salim³

2015

Journal of Computer Science

Self Cite

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“…Hoe [17] introduces the idea of data compression of Electrocardiography (ECG) signals using orthogonal moments such as the Legendre moments of the first kind and the Chebychev moments of the second kind. In [18], an approach is proposed to use discrete orthonormal Tchebichef polynomials for efficient speech recognition. We also previously presented an approach to compress a speech signal without any loss in perceptual quality using Krawtchouk moments [19, 20].…”

Section: Introductionmentioning

confidence: 99%

Enhancing noisy speech signals using orthogonal moments

Jassim¹,

Raveendran²,

Zilany³

2014

IET signal process.

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This study describes a new approach to enhance noisy speech signals using the discrete Tchebichef transform (DTT) and the discrete Krawtchouk transform (DKT). The DTT and DKT are based on well-known orthogonal moments: the Tchebichef and Krawtchouk moments, respectively. The representations of speech signals using a limited number of moment coefficients and their behaviour in the domain of orthogonal moments are shown. The method involves removing noise from the signal using a minimum-mean-square error in the domain of the DTT or DKT. According to comparisons with traditional methods, the initial experiments yield promising results and show that orthogonal moments are applicable in the field of speech signal enhancement. The application of orthogonal moments could be extended to speech analysis, compression and recognition.

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“…Without going into complex field, TMT has been widely used in image and audio processing. For examples, they are used in image Science Publications JCS analysis , texture segmentation, multispectral texture, template matching, pose estimation, pattern recognition, image projection (Abu et al, 2010a), image compression (Ernawan et al, 2011a;Abu et al, 2010b;Lang et al, 2009;Rahmalan et al, 2010), adaptive image compression (Ernawan et al, 2013a), speech recognition (Ernawan et al, 2011b) and vowel recognition (Ernawan et al, 2013b). TMT does not involve any numerical approximation unlike other popular continuous transforms.…”

Section: Introductionmentioning

confidence: 99%

An Image Dithering via Tchebichef Moment Transform

Abu

Ernawan

Suryana

2013

Journal of Computer Science

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Many image display applications and printing devices allow only limited number of colours. They have limited computational power and storage to produce high quality outputs on high bit-depth colour image. A dithering technique is called for here in order to improve the perceptual visual quality of the limited bitdepth images. A dithered image is represented by a natural colour in the low bit depth image colour for displaying and printing. This technique obtains low cost colour image in displaying the colour and printing image pixels. This study proposes the dithering technique based on Tchebichef Moment Transform (TMT) to produce high quality image at low-bit colour. Earlier, a 2×2 Discrete Wavelet Transform (DWT) has been proposed for better image quality on dithering. The 2×2 TMT has been chosen here since it performs better than the 2×2 DWT. TMT provides a compact support on 2×2 blocks. The result shows that 2×2 TMT gives perceptually better quality on colour image dithering in significantly efficient fashion.

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Fast Dynamic Speech Recognition via Discrete Tchebichef Transform

Cited by 5 publications

References 21 publications

Smooth Formant Peak Via Discrete Tchebichef Transform

Smooth Formant Peak Via Discrete Tchebichef Transform

Enhancing noisy speech signals using orthogonal moments

An Image Dithering via Tchebichef Moment Transform

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