An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning

Asif, Amna; Mukhtar, Hamid; Alqadheeb, Fatimah; Ahmad, Hafiz Farooq; Alhumam, Abdulaziz

doi:10.3390/app12010238

Cited by 16 publications

(6 citation statements)

References 37 publications

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“…Arabic short vowels [52] Arabic short vowels using the CNN model are applied to classical Arabic phonemes. The model categorizes 84 classes from 28 consonants associated with 3 short vowels.…”

Section: Methods and Resultsmentioning

confidence: 99%

“…The recognition model also provides satisfactory results. Vowel pronunciation was applied to classical Arabic phonemes [52], which also differs from this research; in that study, the data consisted of 28 consonants associated with 3 short vowels, and a CNN was used to categorize 84 classes. A total of 6229 recorded items in the dataset were documented online from 85 speakers (81 native and 4 nonnative Arabic speakers).…”

Section: Discussionmentioning

confidence: 99%

“…For audio analysis, spectral representations retain more information than traditional hand-crafted features. The spectrograms have fewer dimensions than the raw audio [52].…”

Section: Spectrogram Conversionmentioning

confidence: 99%

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An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition

Rukwong

Pongpinigpinyo

2022

Applied Sciences

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For Thai vowel pronunciation, it is very important to know that when mispronunciation occurs, the meanings of words change completely. Thus, effective and standardized practice is essential to pronouncing words correctly as a native speaker. Since the COVID-19 pandemic, online learning has become increasingly popular. For example, an online pronunciation application system was introduced that has virtual teachers and an intelligent process of evaluating students that is similar to standardized training by a teacher in a real classroom. This research presents an online automatic computer-assisted pronunciation training (CAPT) using deep learning to recognize Thai vowels in speech. The automatic CAPT is developed to solve the inadequacy of instruction specialists and the complex vowel teaching process. It is a unique system that develops computer techniques integrated with linguistic theory. The deep learning model is the most significant part of recognizing vowels pronounced for the automatic CAPT. The major challenge in Thai vowel recognition is the correct identification of Thai vowels when spoken in real-world situations. A convolutional neural network (CNN), a deep learning model, is applied and developed in the classification of pronounced Thai vowels. A new dataset for Thai vowels was designed, collected, and examined by linguists. The result of an optimal CNN model with Mel spectrogram (MS) achieves the highest accuracy of 98.61%, compared with Mel frequency cepstral coefficients (MFCC) with the baseline long short-term memory (LSTM) model and MS with the baseline LSTM model have an accuracy of 94.44% and 90.00% respectively.

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Section: Methods and Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition

Rukwong

Pongpinigpinyo

2022

Applied Sciences

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“…In detail, Ye [11] utilized CycleGAN to conduct style migration on the joint features of CQT features and Mel spectrum; The article [12] employed parallel input training models to improve classification performance; Gajanan [13] performed speech/music classification based on IIR-CQT sound spectrograms. The researchers extracted the frequency-domain features of audio signals and made use of CNN to learn the depth features of each signal so as to achieve the classification [14][15][16]. The article [17] presented a combination of one-dimensional convolutional and bidirectional recurrent neural networks for music style classification; The article [18] used CNN as a feature extractor.…”

Section: Introductionmentioning

confidence: 99%

The Sustainable Development of Intangible Cultural Heritage with AI: Cantonese Opera Singing Genre Classification Based on CoGCNet Model in China

Chen

Zhao

Wang³

et al. 2022

Sustainability

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Chinese Cantonese opera, a UNESCO Intangible Cultural Heritage (ICH) of Humanity, has faced a series of development problems due to diversified entertainment and emerging cultures. While, the management on Cantonese opera data in a scientific manner is conducive to the sustainable development of ICH. Therefore, in this study, a scientific and standardized audio database dedicated to Cantonese opera is established, and a classification method for Cantonese opera singing genres based on the Cantonese opera Genre Classification Networks (CoGCNet) model is proposed given the similarity of the rhythm characteristics of different Cantonese opera singing genres. The original signal of Cantonese opera singing is pre-processed to obtain the Mel-Frequency Cepstrum as the input of the model. The cascade fusion CNN combines each segment’s shallow and deep features; the double-layer LSTM and CNN hybrid network enhance the contextual relevance between signals. This achieves intelligent classification management of Cantonese opera data, meanwhile effectively solving the problem that existing methods are difficult to classify accurately. Experimental results on the customized Cantonese opera dataset show that the method has high classification accuracy with 95.69% Precision, 95.58% Recall and 95.60% F1 value, and the overall performance is better than that of the commonly used neural network models. In addition, this method also provides a new feasible idea for the sustainable development of the study on the singing characteristics of the Cantonese opera genres.

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“…The extracted features are vastly superior over MFCCs with 30 times fewer features to represent the data. Phoneme pronunciation classification was investigated in [10], using CNN architectures and fine-tuning approaches. In [11], voice pathology detection is performed using CNN models.…”

Section: Introductionmentioning

confidence: 99%

Nonwords Pronunciation Classification in Language Development Tests for Preschool Children

Baumann¹,

Wagner²,

Bayerl³

2022

Preprint

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This work aims to automatically evaluate whether the language development of children is age-appropriate. Validated speech and language tests are used for this purpose to test the auditory memory. In this work, the task is to determine whether spoken nonwords have been uttered correctly. We compare different approaches that are motivated to model specific language structures: Low-level features (FFT), speaker embeddings (ECAPA-TDNN), grapheme-motivated embeddings (wav2vec 2.0), and phonetic embeddings in form of senones (ASR acoustic model). Each of the approaches provides input for VGG-like 5-layer CNN classifiers. We also examine the adaptation per nonword. The evaluation of the proposed systems was performed using recordings from different kindergartens of spoken nonwords. ECAPA-TDNN and low-level FFT features do not explicitly model phonetic information; wav2vec2.0 is trained on grapheme labels, our ASR acoustic model features contain (sub-)phonetic information. We found that the more granular the phonetic modeling is, the higher are the achieved recognition rates. The best system trained on ASR acoustic model features with VTLN achieved an accuracy of 89.4% and an area under the ROC (Receiver Operating Characteristic) curve (AUC) of 0.923. This corresponds to an improvement in accuracy of 20.2% and AUC of 0.309 relative compared to the FFT-baseline.

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An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning

Cited by 16 publications

References 37 publications

An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition

An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition

The Sustainable Development of Intangible Cultural Heritage with AI: Cantonese Opera Singing Genre Classification Based on CoGCNet Model in China

Nonwords Pronunciation Classification in Language Development Tests for Preschool Children

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