Energy distribution in formant bands for arabic vowels

Farchi, Mohamed; Tahiry, Karim; Soufyane, mounir; Mounir, Badia; Mouhsen, Ahmed

doi:10.11591/ijece.v9i2.pp1163-1167

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Meanwhile, if the area measurement range for the nine images are greater than the area we specified in Table 1, then it means that this area range is too high, and it will extract the primary foreground objects (huruf) which is not the desired area. Besides, there are two images which are (6,7) in Table 1 that contains missing secondary foreground objects, although the area range for the two images (6,7) are the best area range but the they have many overlapped and connected components in the region which is difficult to recognize between the primary foreground and secondary foreground objects. Therefore, from the area measurements we have conducted, we have noticed that each image has its own area of measurement and its nearly difficult to estimate the best area measurement that can be used for each image to extract the secondary foreground objects from images due to differences of handwritten style, overlapped and several connected components for each image and as well as the different pixels from one image another.…”

Section: Resultsmentioning

confidence: 99%

“…Each letter changes its frame contingent upon its situation in the word. Moreover, [5] writing of Arabic script is normally under indicated for short and long vowels [6] and other markup, alluded to as diacritics. Besides, the Arabic writing has numbers of diacritics, including i'jam ‫ام⟨‬ َ ‫ْج‬ ‫ع‬ ِ ‫,⟩إ‬ and tashkil ‫ِيل⟨‬ ‫ْك‬ ‫َش‬ ‫.⟩ت‬ The latter include the ḥarakāt ‫َات⟨‬ ‫ك‬ َ ‫ر‬ َ ‫.⟩ح‬ Some diacritics in Arabic writing are optional to represent the missing vowels and consonant length.…”

Section: Introductionmentioning

confidence: 99%

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Diacritic segmentation technique for arabic handwritten using region-based

Shiekh

Azmi

Aziz

et al. 2020

IJEECS

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Arabic is a broadly utilized alphabetic composition framework on the planet, and it has 28 essential letters. The letters in order was first used to compose messages in Arabic, most prominently the Qur'an the holy book of Islam. However, Arabic language has diacritics in the word or letters which are not something extra or discretionary to the language, rather they are a vital piece of it. By changing some diacritics may change both the syntax and semantics of a word by turning a word into another. However, the current researches address the foreground image and consider the diacritics as noises or secondary images. Thus, it is not suitable for Arabic handwritten. The diacritics will be removed from the image and this will lead to losing some good features. Furthermore, to extract the diacritics, the region-based segmentation technique is used. The image will be measured based on the region properties by first finding the connected component in binary image, and then we will determine the best area range measurement in that region for each image. The proposed technique region based has been tested in nine different images with different handwritten style, and successfully extracted secondary foreground images (diacritics) for each image.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diacritic segmentation technique for arabic handwritten using region-based

Shiekh

Azmi

Aziz

et al. 2020

IJEECS

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“…Finally, we calculated a 512-point discrete fourier transform. Now, to compute the energy and its distribution for each syllable as shown in Figure 3, we chose six specific bands of frequency as in [59], [60]: ( 1 : 0-400 Hz); 2 : 400-800 Hz; 3 : 800-1200 Hz; 4 : 1200-2000 Hz; 5 : 2000-3500 Hz 6 : 3500-5000 Hz). (1)…”

Section: Computation Of Logarithmic Energy Characteristics Based On Dftmentioning

confidence: 99%

Emotion recognition from syllabic units using k-nearest-neighbor classification and energy distribution

Agrima¹,

Mounir

Farchi³

et al. 2021

IJECE

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In this article, we present an automatic technique for recognizing emotional states from speech signals. The main focus of this paper is to present an efficient and reduced set of acoustic features that allows us to recognize the four basic human emotions (anger, sadness, joy, and neutral). The proposed features vector is composed by twenty-eight measurements corresponding to standard acoustic features such as formants, fundamental frequency (obtained by Praat software) as well as introducing new features based on the calculation of the energies in some specific frequency bands and their distributions (thanks to MATLAB codes). The extracted measurements are obtained from syllabic units’ consonant/vowel (CV) derived from Moroccan Arabic dialect emotional database (MADED) corpus. Thereafter, the data which has been collected is then trained by a k-nearest-neighbor (KNN) classifier to perform the automated recognition phase. The results reach 64.65% in the multi-class classification and 94.95% for classification between positive and negative emotions.

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“…This shows that, there is still hope to help people with physical disabilities especially for those on smart wheelchair where they can use their own voice to ease their movements from one location to another. Most of the voice-recognition programs are in English [6] and there is a limited study conducted on other languages such as the Malay language.…”

Section: Introductionmentioning

confidence: 99%

Voiced and unvoiced separation in malay speech using zero crossing rate and energy

Hanifa

Isa

Mohamad

et al. 2019

IJEECS

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<p>This paper contributes to the literature on voice-recognition in the context of non-English language. Specifically, it aims to validate the techniques used to present the basic characteristics of speech, viz. voiced and unvoiced, that need to be evaluated when analysing speech signals. Zero Crossing Rate (ZCR) and Short Time Energy (STE) are used in this paper to perform signal pre-processing of continuous Malay speech to separate the voiced and unvoiced parts. The study is based on non-real time data which was developed from a collection of audio speeches. The signal is assessed using ZCR and STE for comparison purposes. The results revealed that ZCR are low for voiced part and high for unvoiced part whereas the STE is high for voiced part and low for unvoiced part. Thus, these two techniques can be used effectively for separating voiced and unvoiced for continuous Malay speech.</p>

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Energy distribution in formant bands for arabic vowels

Cited by 5 publications

References 4 publications

Diacritic segmentation technique for arabic handwritten using region-based

Diacritic segmentation technique for arabic handwritten using region-based

Emotion recognition from syllabic units using k-nearest-neighbor classification and energy distribution

Voiced and unvoiced separation in malay speech using zero crossing rate and energy

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