Turkish vowel classification based on acoustical and decompositional features optimized by Genetic Algorithm

Korkmaz, Yunus; Boyacı, Aytuğ; Tuncer, Türker

doi:10.1016/j.apacoust.2019.04.027

Cited by 17 publications

(14 citation statements)

References 15 publications

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“…the predicting system has an accuracy of 98.68%. The features MFCC, linear predictive coding (LPC), energy, zero-crossing rate (ZCR), and Shannon entropy have been used in many speech signal studies, either for the detection of Parkinson's disease as in [8], [9] or either for recognization [10]. In the paper, Oung et al [11] proposed a detection and a classification system of the Parkinson's disease centered on empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT).…”

Section: Introductionmentioning

confidence: 99%

“…The genetic algorithm (GA) has the main role to overcome the optimization problems. In the paper [9], [10] the genetic algorithm is applied for the purpose of selecting the convenient features to reach the most accurate prediction system. In the paper, Umar and Felemban [12] used the GA to execute cyber attacks as false data injection attacks (FDIA) in the power systems.…”

Section: Introductionmentioning

confidence: 99%

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Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

Nsiri

Soumaya

Taoufiq³

et al. 2022

IJECE

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Among the several ways followed for detecting Parkinson's disease, there is the one based on the speech signal, which is a symptom of this disease. In this paper focusing on the signal analysis, a data of voice records has been used. In these records, the patients were asked to utter vowels “a”, “o”, and “u”. Discrete wavelet transforms (DWT) applied to the speech signal to fetch the variable resolution that could hide the most important information about the patients. From the approximation a3 obtained by Daubechies wavelet at the scale 2 level 3, 21 features have been extracted: a <a name="_Hlk88480766"></a>linear predictive coding (LPC), energy, zero-crossing rate (ZCR), mel frequency cepstral coefficient (MFCC), and wavelet Shannon entropy. Then for the classification, the K-nearest neighbour (KNN) has been used. The KNN is a type of instance-based learning that can make a decision based on approximated local functions, besides the ensemble learning. However, through the learning process, the choice of the training features can have a significant impact on overall the process. So, here it stands out the role of the genetic algorithm (GA) to select the best training features that give the best accurate classification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

Nsiri

Soumaya

Taoufiq³

et al. 2022

IJECE

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“…Moreover, Turkish is a language that is read as it is written, with each letter representing a different sound. The Turkish language has 2 nasal consonants (/m/ and /η/), 8 oral vowels, and no diphthongs or nasalized vowels [15].…”

Section: Speech Samplesmentioning

confidence: 99%

Comparing the Temporal Aspects of Velopharyngeal Closure in Children with and without Cleft Palate

Özkan

Kayikci

Günaydın

et al. 2021

Folia Phoniatr Logop

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Introduction: Children with cleft palate exhibit differences in the 4 temporal components of nasalization (nasal onset and offset intervals, nasal consonant duration, and total speech duration), with various patterns having been noted based on different languages. Thus, the current study aimed to examine the temporal aspects of velopharyngeal closure in children with and without cleft palate; this is the first study to do so in the Turkish language. Methods: This study evaluated and compared the 4 temporal characteristics of velopharyngeal closure in children (aged 6–10 years) with (n = 28) and without (n = 28) cleft palate using nonword consonant and vowel speech samples, including the bilabial nasal-to-stop combination /mp/ and the velar nasal-to-stop combination /ηk/. Acoustic data were recorded using a nasometer, after which acoustic waveforms were examined to determine the 4 temporal components of nasalization. Flexible nasoendoscopy was then used to evaluate velopharyngeal closure patterns. Results: With regard to the 4 closure patterns, significant differences in the nasal offset interval (F4–25 = 10.213, p = 0.04; p < 0.05) and the nasal consonant duration ratio (F4–25 = 12.987, p = 0.02; p < 0.05) were observed for only /ampa/. The coronal closure pattern showed the longest closure duration (0.74 s). Children with cleft palate showed prolonged temporal parameters in all 4 characteristics, reflecting oral-nasal resonance imbalances. In particular, the low vowel sound /a/ was significantly more prolonged than the high vowel sounds /i/ and /u/. Conclusions: The examined temporal parameters offer more accurate characterizations of velopharygeal closure, thereby allowing more accurate clinical assessments and more appropriate treatment procedures. Children with cleft palate showed longer nasalization durations compared to those without the same. Thus, the degree of hypernasality in children with cleft palate may affect the temporal aspects of nasalization.

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“…In [22], a deep neural network is used to predict historical phonetic features drawn upon synchronic phonetic patterns arising from coarticulation and statistical constraints in Proto-Indo-European language. In [23], extracted acoustic features of speech signal using hamming window and pre-emphasis filter, in addition to extracted decompositional features using daubechies-filtered 5th-depth Wavelet Packet Decomposition (WPT), are optimized using genetic algorithm to classify Turkish vowels.…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimizing Arabic Speech Distinctive Phonetic Features and Phoneme Recognition Using Genetic Algorithm

et al. 2020

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Distinctive phonetic features have an important role in Arabic speech phoneme recognition. In a given language, distinctive phonetic features are extrapolated from acoustic features using different methods. However, exploiting lengthy acoustic features vector in the sake of phoneme recognition has a huge cost in terms of computational complexity, which in turn, affects real time applications. The aim of this work is to consider methods to reduce the size of features vector employed for distinctive phonetic feature and phoneme recognition. The objective is to select the relevant input features that contribute to the speech recognition process. This, in turn, will lead to a reduced computational complexity of recognition algorithm, and an improved recognition accuracy. In the proposed approach, genetic algorithm is used to perform optimal features selection. Therefore, a baseline model based on feedforward neural networks is first built. This model is used to benchmark the results of proposed features selection method with a method that employs all elements of a features vector. Experimental results, utilizing the King Abdulaziz City for Science and Technology Arabic Phonetic Database, show that the average genetic algorithm based phoneme overall recognition accuracy is maintained slightly higher than that of recognition method employing the full-fledge features vector. The genetic algorithm based distinctive phonetic features recognition method has achieved a 50% reduction in the dimension of the input vector while obtaining a recognition accuracy of 90%. Moreover, the results of the proposed method is validated using Wilcoxon signed rank test.

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Turkish vowel classification based on acoustical and decompositional features optimized by Genetic Algorithm

Cited by 17 publications

References 15 publications

Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

Comparing the Temporal Aspects of Velopharyngeal Closure in Children with and without Cleft Palate

Optimizing Arabic Speech Distinctive Phonetic Features and Phoneme Recognition Using Genetic Algorithm

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