On Vowels Segmentation and Identification Using Formant Transitions in Continuous Recitation of Quranic Arabic

Iqbal, Hafiz Rizwan; Awais, Mian Muhammad; Masud, Shahid; Shamail, Shafay

doi:10.1007/978-3-540-79355-7_15

Cited by 17 publications

(5 citation statements)

References 2 publications

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“…The recognition of vowels by using formant frequencies in Arabic speech is described in [5] and [6]. In [5] authors have described research in segmentation and identification of Arabic vowels in continuous speech based on transitions in formant frequencies. They have developed the recognition system with accuracy up to 90% from the speech with 1000 vowels.…”

Section: Current Research In Continuous Speech Recognitionmentioning

confidence: 99%

Recognition of vowels in continuous speech by using formants

Prica

Ilić

2010

FACTA U EE

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Speech consists of acoustic pressure waves created by the voluntary movements of anatomical structures in the human speech production system. These waveforms are broadly classified into voiced and unvoiced speech. Voiced sounds (vowels for example), produce quasi-periodic pulses of air which are acoustically filtered as they propagate through the vocal tract. The main distinction between vowels and consonants is that vowels resonate in the throat. Formants are exactly the resonant frequencies of a vocal tract when pronouncing a vowel. In this paper we attempt to carry out Vowel Recognition through Formant Analysis in Serbian language, wherein we detect which of the five Serbian vowels is spoken by the Speaker. Here we describe a standard approach for classification of vowels in continuous speech based on three formants: F1, F2 and F3. We have investigated the correlations between formants in each vowel and developed the algorithm to reduce the overlap of different vowels in F1-F2 and F2-F3 planes.

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Section: Current Research In Continuous Speech Recognitionmentioning

confidence: 99%

Recognition of vowels in continuous speech by using formants

Prica

Ilić

2010

FACTA U EE

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“…Both nasal and lateral formant F 2 is not in the range as result discussed by Iqbal (2008) as shown in Table 8. This might be the effect of the vocal tract length since the study used children age 7 to 11-year-old, while Iqbal used expertise in Quranic recitation age 15 to 30 years old.…”

Section: Resultsmentioning

confidence: 69%

“…The inner the place of articulation, the results is higher in formant F 2 . Iqbal et al (2008) used formant frequencies to identify Arabic vowels and nasal formants /lam/, ‫,]ل[‬ /mim/, ‫]م[‬ and /nun/, ‫. ]ن[‬ Subjects were Quranic expertise which gave 90% average accuracy.…”

Section: Introductionmentioning

confidence: 99%

Difficulties of Standard Arabic Phonemes Spoken by Non-Arab Primary School Children based on Formant Frequencies

Abdul-Kadir

Sudirman²

2011

Journal of Computer Science

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“…More recently, Iqbal et al (2008) reported a new preliminary study on vowels segmentation and identification using formant transitions occurring in continuous recitation of Quranic Arabic. Their algorithm extracted the formants of pre-segmented recitation audio files and recognized the vowels on the basis of these extracted formants.…”

Section: Spoken Arabic Digits and Vowelsmentioning

confidence: 99%

Comparing ANN to HMM in implementing limited Arabic vocabulary ASR systems

Alotaibi

2011

Int J Speech Technol

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In this paper we investigated Artificial Neural Networks (ANN) based Automatic Speech Recognition (ASR) by using limited Arabic vocabulary corpora. These limited Arabic vocabulary subsets are digits and vowels carried by specific carrier words. In addition to this, Hidden Markov Model (HMM) based ASR systems are designed and compared to two ANN based systems, namely Multilayer Perceptron (MLP) and recurrent architectures, by using the same corpora. All systems are isolated word speech recognizers. The ANN based recognition system achieved 99.5% correct digit recognition. On the other hand, the HMM based recognition system achieved 98.1% correct digit recognition. With vowels carrier words, the MLP and recurrent ANN based recognition systems achieved 92.13% and 98.06, respectively, correct vowel recognition; but the HMM based recognition system achieved 91.6% correct vowel recognition.

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On Vowels Segmentation and Identification Using Formant Transitions in Continuous Recitation of Quranic Arabic

Cited by 17 publications

References 2 publications

Recognition of vowels in continuous speech by using formants

Recognition of vowels in continuous speech by using formants

Difficulties of Standard Arabic Phonemes Spoken by Non-Arab Primary School Children based on Formant Frequencies

Comparing ANN to HMM in implementing limited Arabic vocabulary ASR systems

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