Review of distinctive phonetic features and the Arabic share in related modern research

Alotaibi, Yousef Ajami; Meftah, Ali H.

doi:10.3906/elk-1112-29

Cited by 14 publications

(27 citation statements)

References 28 publications

(25 reference statements)

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“…A review of the distinctive phonetic features (DPF) is presented in [ 14 ]. The DPF elements can be used to distinguish between phonemes, where each phoneme is represented by a vector of the presence and absence of these DPF elements.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The DPF elements can be used to distinguish between phonemes, where each phoneme is represented by a vector of the presence and absence of these DPF elements. Based on data from Arabic linguistics and researchers, [ 14 ] showed the common DPF for modern standard Arabic. According to these DPFs, a recent study of modeling and extracting them using deep neural network and multi-layer perceptron is presented in [ 3 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to these DPFs, a recent study of modeling and extracting them using deep neural network and multi-layer perceptron is presented in [ 3 ]. Experiments were conducted using the KACST Arabic Phonetic Database (KAPD) corpus to extract the 31 DPF elements presented in a previous study [ 14 ]. A separate classifier for each DPF element was designed to extract the 31 binary values that represent the existence and absence of each DPF from 15 frames per phoneme.…”

Section: Literature Reviewmentioning

confidence: 99%

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Deep Learning-Based Detection of Articulatory Features in Arabic and English Speech

Algabri

Mathkour

Alsulaiman

et al. 2021

Sensors

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This study proposes using object detection techniques to recognize sequences of articulatory features (AFs) from speech utterances by treating AFs of phonemes as multi-label objects in speech spectrogram. The proposed system, called AFD-Obj, recognizes sequence of multi-label AFs in speech signal and localizes them. AFD-Obj consists of two main stages: firstly, we formulate the problem of AFs detection as an object detection problem and prepare the data to fulfill requirement of object detectors by generating a spectral three-channel image from the speech signal and creating the corresponding annotation for each utterance. Secondly, we use annotated images to train the proposed system to detect sequences of AFs and their boundaries. We test the system by feeding spectrogram images to the system, which will recognize and localize multi-label AFs. We investigated using these AFs to detect the utterance phonemes. YOLOv3-tiny detector is selected because of its real-time property and its support for multi-label detection. We test our AFD-Obj system on Arabic and English languages using KAPD and TIMIT corpora, respectively. Additionally, we propose using YOLOv3-tiny as an Arabic phoneme detection system (i.e., PD-Obj) to recognize and localize a sequence of Arabic phonemes from whole speech utterances. The proposed AFD-Obj and PD-Obj systems achieve excellent results for Arabic corpus and comparable to the state-of-the-art method for English corpus. Moreover, we showed that using only one-scale detection is suitable for AFs detection or phoneme recognition.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Deep Learning-Based Detection of Articulatory Features in Arabic and English Speech

Algabri

Mathkour

Alsulaiman

et al. 2021

Sensors

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“…This paper reports the work of modeling DPFs of Modern Standard Arabic (MSA). The DPF elements considered in this study are listed in Table 1 [1].…”

Section: A Background On Dpfsmentioning

confidence: 99%

“…Other relevant and highly descriptive features that represent speech waveform are the distinctive phonetic features (DPFs) [1] that are the main focus of this paper. A DPF vector is a set of binary elements that uniquely describes the articulatory and phonetic properties of phonemes [1]. It describes the presence or absence of certain elements of a language phonology by marking them, conventionally, as ''+'' or ''-'', respectively.…”

Section: Introductionmentioning

confidence: 99%

Distinctive Phonetic Features Modeling and Extraction Using Deep Neural Networks

et al. 2019

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Feature extraction is a critical stage of digital speech processing systems. Quality of features is of great importance to provide a solid foundation upon which the subsequent stages stand. Distinctive phonetic features (DPFs) are one of the most representative features of the speech signals. The significance of DPFs is in their ability to provide abstract description of the places and manners of articulation of the language phonemes. A phoneme's DPF element reflects unique articulatory information about that phoneme. Therefore, there is a need to discover and investigate each DPF element individually in order to achieve a deeper understanding and to come up with a descriptive model for each one. Such fine-grained modeling will satisfy the uniqueness of each DPF element. In this paper, the problem of DPF modeling and extraction of modern standard Arabic is tackled. Due to the remarkable success of deep neural networks (DNNs) that are initialized using deep belief networks (DBNs) in serving DSP applications and its capability of extracting highly representative features from the raw data, we exploit its modeling power to investigate and model the DPF elements. DNN models are compared with the classical multilayer perceptron (MLP) models. The representativeness of several acoustic cues for different DPF elements was also measured. This paper is based on formalizing DPF modeling problem as a binary classification problem. Because the DPF elements are highly imbalanced data, evaluating the quality of models is a very tricky process. This paper addresses the proper evaluation measures satisfying the imbalanced nature of the DPF elements. After modeling each element individually, the two top-level DPF extractors are designed: MLP-and DNN-based extractors. The results show the quality of DNN models and their superiority over MLPs with accuracies of 89.0% and 86.7%, respectively.INDEX TERMS Modern standard Arabic, distinctive phonetic features, speech processing, deep belief networks, restricted Boltzmann machine.YASSER SEDDIQ received the B.S. degree in computer engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, in 2004, and the M.S. degree in computer engineering and the Ph.D. degree in computer and information sciences (computer engineering) from King Saud University (KSU), Riyadh, Saudi Arabia, in 2010 and 2017, respectively. He is currently an Assistant Research Professor with the King Abdulaziz City for Science and Technology (KACST), Riyadh. His research interests include digital signal processing, speech processing, image processing, computer arithmetic, and digital systems design using FPGA.

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Gender Identification from Arabic Speech Using Machine Learning

Hamdi

Moussaoui

Oussalah

et al. 2020

Modelling and Implementation of Complex Systems

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Speech recognition is becoming increasingly used in realworld applications. One of the interesting applications is automatic gender recognition which aims to recognize male and female voices from short speech samples. This can be useful in applications such as automatic dialogue systems, system verification, prediction of demographic attributes (e.g., age, location) and estimating person's emotional state. This paper focuses on gender identification from the publicly available dataset Arabic Natural Audio Dataset (ANAD) using an ensembleclassifier based approach. More specifically, initially we extended the original ANAD to include a gender label information through a manual annotation task. Next, in order to optimize the feature engineering process, a three stage machine learning approach is devised. In the first phase, re restricted to features to the two widely used ones; namely, MFCC and fundamental frequency coefficients. In the second phase, six distinct acoustic features were employed. Finally, in the third phase, the features were selected according to their associated weights in Random Forest Classifier, and the best features are thereby selected. The latter approach enabled us to achieve a classification rate of 96.02% on the test set generated with linear SVM classifier.

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Review of distinctive phonetic features and the Arabic share in related modern research

Cited by 14 publications

References 28 publications

Deep Learning-Based Detection of Articulatory Features in Arabic and English Speech

Deep Learning-Based Detection of Articulatory Features in Arabic and English Speech

Distinctive Phonetic Features Modeling and Extraction Using Deep Neural Networks

Gender Identification from Arabic Speech Using Machine Learning

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