Arabic vowels recognition based on wavelet average framing linear prediction coding and neural network

Daqrouq, Khaled; Azzawi, Khalooq Y. Al

doi:10.1016/j.specom.2013.01.002

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…The role of confidence interval is to give an estimated range of possible values including an unknown population parameter, a range estimate for a given set from the sample data. A confidence interval for the recognition rates of the sets combination mean value μ and Standard deviation σ are based on samples size n, therefore, where C is the critical value for a 95% confidence interval (1.96) [ 39 , 40 ]. Confidence interval results were calculated and reported; specifically, the confidence interval states that 95% of the calculated recognition rate for each combination should be contained in this interval.…”

Section: Resultsmentioning

confidence: 99%

“…For more in-depth analysis of the results achieved in Table 2 , BERWE2, TERWE2 and FERWE2 are tested and compared further. In order to compare the obtained results, we performed recognition sensitivity (RS) [ 40 ] analysis, which was defined as the following: where ρ XX is the correlation coefficient determined for two PDZ domain feature vectors belonging to the same class and ρ XY is the correlation coefficient determined for two PDZ domain feature vectors belonging to two different classes. The results representing the recognition sensitivity were determined for 30 combinations of different PDZ domains indicating that these three methods are similar and comparable.…”

Section: Resultsmentioning

confidence: 99%

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Application of Wavelet Transform for PDZ Domain Classification

et al. 2015

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PDZ domains have been identified as part of an array of signaling proteins that are often unrelated, except for the well-conserved structural PDZ domain they contain. These domains have been linked to many disease processes including common Avian influenza, as well as very rare conditions such as Fraser and Usher syndromes. Historically, based on the interactions and the nature of bonds they form, PDZ domains have most often been classified into one of three classes (class I, class II and others - class III), that is directly dependent on their binding partner. In this study, we report on three unique feature extraction approaches based on the bigram and trigram occurrence and existence rearrangements within the domain's primary amino acid sequences in assisting PDZ domain classification. Wavelet packet transform (WPT) and Shannon entropy denoted by wavelet entropy (WE) feature extraction methods were proposed. Using 115 unique human and mouse PDZ domains, the existence rearrangement approach yielded a high recognition rate (78.34%), which outperformed our occurrence rearrangements based method. The recognition rate was (81.41%) with validation technique. The method reported for PDZ domain classification from primary sequences proved to be an encouraging approach for obtaining consistent classification results. We anticipate that by increasing the database size, we can further improve feature extraction and correct classification.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Application of Wavelet Transform for PDZ Domain Classification

et al. 2015

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“…Research on vowels in various Arabic dialects investigated by [6] proven that Arabic vowels pronounced by speakers of different Arabic dialects including Saudi, Sudanese and Egyptian dialects has a different formant for each vowel. Conversely, a recognition system is developed by [7] to recognise Arabic vowels using wavelet average framing linear prediction coding, which is a modified version of the linear prediction coding and the probabilistic neural network. Another Arabic vowel recognition system is suggested by [8] based on facial electromyograph signals (EMG) in order to use it for computer interface and subjects with speech weakness.…”

Section: Introductionmentioning

confidence: 99%

Acoustical Comparison between /u/ and /u:/ Arabic Vowels for Non-Native Speakers

Almisreb¹,

Tahir²,

Abidin³

et al. 2018

IJEECS

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<p>The articulation of Arabic phonemes is essential for the Malay community since Arabic language is mandatory to perform worship. Hence, in this paper, an acoustical analysis of Arabic phonemes for vowels /u/ and /u:/ is discussed based on tokens pronounced by Malay speakers. The experimental results showed that the Malay speakers are inclined to utter these Arabic phonemes similar to the native speakers and it was also found from the analysis that the vowel /u/ and /u: was articulated as high-back vowels. Conversely, the vowel /u/ was located lower than /u:/ as in the vowel-space. Alternatively results also showed that /u/ and /u:/ is higher than the other vowels specifically /a/ and /a:/. In addition, the statistical analysis showed that the formant frequencies of both short and long dummah for formant frequency F1, F2 and F3 have more variation in terms of /u/ as compare to /u:/. In contrast formant frequency F4 and F5 are more diversity in terms of /u:/.</p>

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“…Since a very good vowel recognition system is an important part in speech recognition system, there are several research works on vowel recognition in different languages including English [2][3][4][5], English with Malaysian speakers [6], Arabic [7], Mandarin [8], Kazakh [9], Malay [10], Thai [11], etc. There are also some research works on the Japanese vowel sound (/a/, /i/, /u/, /e/, and /o/) recognition system [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Japanese Vowel Sound Classification Using Fuzzy Inference System

Phitakwinai¹,

Sawada²,

Auephanwiriyakul³

et al. 2014

Journal of the Korea Convergence Society

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An automatic speech recognition system is one of the popular research problems. There are many research groups working in this field for different language including Japanese. Japanese vowel recognition is one of important parts in the Japanese speech recognition system. The vowel classification system with the Mamdani fuzzy inference system was developed in this research. We tested our system on the blind test data set collected from one male native Japanese speaker and four male non-native Japanese speakers. All subjects in the blind test data set were not the same subjects in the training data set. We found out that the classification rate from the training data set is 95.0 %. In the speaker-independent experiments, the classification rate from the native speaker is around 70.0 %, whereas that from the non-native speakers is around 80.5 %.

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Arabic vowels recognition based on wavelet average framing linear prediction coding and neural network

Cited by 7 publications

References 38 publications

Application of Wavelet Transform for PDZ Domain Classification

Application of Wavelet Transform for PDZ Domain Classification

Acoustical Comparison between /u/ and /u:/ Arabic Vowels for Non-Native Speakers

Japanese Vowel Sound Classification Using Fuzzy Inference System

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