Pronunciation Of Phonemes Research Articles

The Pronunciation Problems Encounter EFL Learners in Spoken English at University

This research examines the pronunciation difficulties of EFL undergraduate learners at universities when pronouncing English phonemes. It collects data using recorded speech samples, pronunciation tests and questionnaires as part of a research method for collecting data. The instruments used for this study are collecting data and analyzing them. The test materials are prepared in speech levels. Therefore, the observations of these were based on auditory impressions. The results suggest that the participants had a pronunciation challenge with the phonemes in English. This research gives insights and supports ESL teachers in developing teaching techniques that will lessen or eliminate future issues with Arabic speakers' pronunciation of English phonemes.

ANALISIS KESULITAN BELAJAR MENULIS CERITA NARASI BAHASA JAWA PESERTA DIDIK KELAS V DI SDN SUKOHARJO 01 PATI

Research regarding the difficulties of learning to write Javanese narrative stories needs to be carried out because participants may have difficulty learning to write Javanese narrative stories. This research aims to determine (1) the ability of fifth grade students in writing narrative stories in Javanese; (2) form of difficulty learning to write Javanese narrative stories; (3) factors causing difficulties in learning to write Javanese stories. The research method used is a qualitative method. The research results include: (1) students' ability to write Javanese narrative stories is still low; (2) the forms of difficulty experienced by students occur in arranging words according to base language, pronunciation of phonemes and writing words in Javanese, as well as determining punctuation marks and capital letters; (3) The factors causing difficulty in writing narrative stories in Javanese are a lack of interest in learning Javanese and unfamiliarity with using Javanese in everyday life.

ANALYSIS OF PHONOLOGICAL ERRORS IN MAHARAH KALAM PRESENTATION OF NAHWU WADHIFI BOOK

The lack of attention to the Maharah Kalam has made many Arabic language scholars unable to communicate using Arabic. Insufficient self-confidence, fear of being wrong in terms of rules and pronunciation create obstacles for students to speak Arabic, therefore an It is necessary to analyze linguistic faults so that Arabic pronunciation pays more attention and in order to avoid mistakes in Arabic pronunciation. The purpose of this study is to outline the types of phonological errors in Maharah Kalam, as well as to analyze phonological errors in student Maharah Kalam. This research is field research and is a qualitative descriptive study that employs a linguistic error analysis approach. Ways for gathering data that involve observation and recording. This observation is carried out by paying attention to students' speech and noting any grammar mistakes. Documentation is completed using recording technology. According to the study's findings, consonant sound alterations represent faults at the phonological (phoneme) level, including: changes in consonant /ث/ to consonant /س/, consonant /ع/ to consonant /أ/, consonant /ص/ becomes a consonant /س/. The other errors are errors in reducing phoneme sounds, phonological mistakes at the level of syntax, and phonological mistakes at the morphological level. It can be concluded that there are several errors in language and the factor causing language errors is a lack of attention to the language rules that should be applied. Apart from that, there is the influence of the student's mother tongue or second language, and the influence of the environment, both formal and informal, as well as the lack of application of Arabic. Several causal factors have a big influence on students' phoneme pronunciation errors. This research can provide a new platform for Arabic speakers to always pay attention to Arabic rules when speaking.

Influence of Mother Tongue on Phoneme Pronunciation in Speaking Mandarin for IX Grade Students of SMP Tunas Bangsa Makassar

This research is a qualitative descriptive study that aims to obtain data on the influence of mother tongue on the pronunciation of phonemes in speaking Mandarin for class IX students of SMP Tunas Bangsa Makassar. The data of this research is the pronunciation of segmental phonemes including consonants and vowels in Mandarin. The research data was obtained through audio recordings related to the oral test activities carried out by students. The data that has been obtained is then analyzed using an assessment table, conversion table and Foreign Service Institute table. From the results of data analysis, it is known that there is an influence of mother tongue on the pronunciation of phonemes in speaking Mandarin for class IX students of SMP Tunas Bangsa Makassar by obtaining FSI 1+ level, namely in the category of students being able to initiate and follow conversations and socialize on a limited basis.

Perubahan Fonem pada Penderita Gangguan Berbahasa Bibir Sumbing

Cleft lip and palate sufferers often experience language disorders that can affect aspects of their communication. An in-depth understanding of language disorders in this age group is important to provide appropriate interventions and improve their quality of life. This study identified and analyzed language disorders in cleft lip and palate sufferers at the age of 23 years. This study uses a qualitative method. Data was collected by searching for data sources on YouTube, listening intensively and transcribing into written form, marking and noting things related to phoneme changes (dyslalia). The data analysis technique in this study is to classify data according to language disorders in cleft lip and palate sufferers, then analyzed in relation to language disorders with phoneme changes (dyslalia). The results of the research found changes, omissions and unclear pronunciation of phonemes (dyslalia) in several words spoken by cleft lip and palate sufferers.

Decoding Single and Paired Phonemes Using 7T Functional MRI

Several studies have shown that mouth movements related to the pronunciation of individual phonemes are represented in the sensorimotor cortex. This would theoretically allow for brain computer interfaces that are capable of decoding continuous speech by training classifiers based on the activity in the sensorimotor cortex related to the production of individual phonemes. To address this, we investigated the decodability of trials with individual and paired phonemes (pronounced consecutively with one second interval) using activity in the sensorimotor cortex. Fifteen participants pronounced 3 different phonemes and 3 combinations of two of the same phonemes in a 7T functional MRI experiment. We confirmed that support vector machine (SVM) classification of single and paired phonemes was possible. Importantly, by combining classifiers trained on single phonemes, we were able to classify paired phonemes with an accuracy of 53% (33% chance level), demonstrating that activity of isolated phonemes is present and distinguishable in combined phonemes. A SVM searchlight analysis showed that the phoneme representations are widely distributed in the ventral sensorimotor cortex. These findings provide insights about the neural representations of single and paired phonemes. Furthermore, it supports the notion that speech BCI may be feasible based on machine learning algorithms trained on individual phonemes using intracranial electrode grids.

Some Language Problems in the Spelling of Primary School Students

This paper addresses language problems among primary school students, particularly focusing on spelling. The research encompasses a broad spectrum of linguistic aspects, including the pronunciation of phonemes and morphemes, the utilization of grammatical forms, as well as spelling and language use. A thorough study was conducted in two primary schools involving fourth and fifth-grade students, to identify language delays, particularly in spelling and pronunciation. The comparison between these schools indicates more frequent delays in the linguistic skills of students at this age. The rules governing spelling and pronunciation play a crucial role in enhancing the linguistic culture of students. Nevertheless, the primary influence on a student’s language originates from their family and cultural background, expanding and evolving further through social interactions and the educational process. The paper conducts a comprehensive analysis of students’ language, encompassing oral expression, various tasks, literary works, phonetics, and grammar. It emphasizes that linguistic mistakes made by students in their family environment can persist and impact their primary education. The discussion also delves into possibilities for avoiding these language mistakes. 
 
 Received: 8 September 2023 / Accepted: 24 December 2023 / Published: 5 January 2024

Effective Phoneme Decoding With Hyperbolic Neural Networks for High-Performance Speech BCIs.

Speech brain-computer interfaces (speech BCIs), which convert brain signals into spoken words or sentences, have demonstrated great potential for high-performance BCI communication. Phonemes are the basic pronunciation units. For monosyllabic languages such as Chinese Mandarin, where a word usually contains less than three phonemes, accurate decoding of phonemes plays a vital role. We found that in the neural representation space, phonemes with similar pronunciations are often inseparable, leading to confusion in phoneme classification. We mapped the neural signals of phoneme pronunciation into a hyperbolic space for a more distinct phoneme representation. Critically, we proposed a hyperbolic hierarchical clustering approach to specifically learn a phoneme-level structure to guide the representation. We found such representation facilitated greater distance between similar phonemes, effectively reducing confusion. In the phoneme decoding task, our approach demonstrated an average accuracy of 75.21% for 21 phonemes and outperformed existing methods across different experimental days. Our approach showed high accuracy in phoneme classification. By learning the phoneme-level neural structure, the representations of neural signals were more discriminative and interpretable. Our approach can potentially facilitate high-performance speech BCIs for Chinese and other monosyllabic languages.

A Description of Japanese Sound System

This paper explores various aspects of the Japanese sound system, including dialect divisions, languages in daily contact with Japanese, consonants, vowels, and pitch. It examines the historical records of dialects in Japan, highlighting the formation and characteristics of major dialects such as the Tokyo, Kansai, Hokkaido, and Kyushu dialects. The paper also discusses the influence of loanwords on the Japanese language, emphasizing the increasing presence of loanwords in daily communication. Furthermore, it analyzes the consonant and vowel phonemes in Japanese, paying attention to their variations and pronunciation differences, including the shape of the lips during vowel pronunciation and the pronunciation of special phonemes in Japanese, as well as the influence of the position of the tongue on the pronunciation of Japanese vowels. Lastly, the paper delves into the importance of pitch in Japanese, both phonetically and phonologically, and its role in word formation, meaning, and sentence-level intonation. By exploring these aspects, this paper provides a comprehensive understanding of the Japanese sound system and its significance in the language.

Arabic Phoneme Learning Challenges for Madurese Students and the Solutions

This article discussed the challenges in pronouncing Arabic phonemes by students at INSTIKA Madura. Phoneme pronunciation is the most important principle in Arabic. Without the correct phoneme pronunciation, a language cannot be understood. The problem of phoneme pronunciation was investigated and a solution was found based on factor analysis. Qualitative descriptive research design was used with a case study approach. Data collection methods include interviews with lecturers and students, direct observation of in-class learning and documentation of the results of lecturer notes. The data analysis model adhered the interactive model of Miles, Huberman, and Saldana. Validity was ensured through passion, observation, triangulation, and expert views. The finding of the research showed problems with Arabic phonemes, which were categorized as Akhtha’ al-Harakat, Akhtha’ al-Ibdal, Akhtha’ al-Hadzf, and Akhtha’ al-Tahrif. Factors include language problems (characteristics of the first and second languages) and non-language problems (student characteristics, lecturer competence, learning strategies, lesson materials, and learning facilities). The proposed solutions included error and comparative analysis for language problems, motivation, diagnosis, cooperative learning, detailed examples, pronunciation exercises, and adequate facilities for non-language problems. This research provided a comprehensive study of the challenges of pronouncing Arabic phonemes at INSTIKA Madura. Certain types of errors and the underlying factors that affect pronunciation were identified. Practical solutions were proposed, addressing both language and non-language aspects to improve students' pronunciation skills. These findings offered valuable insights for educators, curriculum developers and language instructors, facilitating targeted interventions and effective teaching strategies to students struggling with Arabic phonetics.

A Comparative Study on Selecting Acoustic Modeling Units for WFST-based Mongolian Speech Recognition

Traditional weighted finite-state transducer– (WFST) based Mongolian automatic speech recognition (ASR) systems use phonemes as pronunciation lexicon modeling units. However, Mongolian is an agglutinative, low-resource language, and building an ASR system based on the phoneme pronunciation lexicon remains a challenge for various reasons. First, the phoneme pronunciation lexicon manually constructed by Mongolian linguists is finite, which is usually used to build a grapheme-to-phoneme conversion (G2P) model to frequently expand new words. However, the data sparsity decreases the robustness of the G2P model and affects the performance of the final ASR system. Second, homophones and polysyllabic words are common in Mongolian, which has a certain impact on the construction of the Mongolian acoustic model. To address these problems, in this work, we first propose a grapheme-to-phoneme alignment model to obtain the mapping relationship between phonemes and subword units. Then, we construct an acoustic subword segmentation set to segment words directly instead of using the traditional G2P method to predict phoneme sequences to expand the pronunciation lexicon. Further, by analyzing the Mongolian encoding form, we also propose an acoustic subword modeling units construction method that removes control characters. Finally, we investigate various acoustic subword modeling units for pronunciation lexicon construction for the Mongolian ASR system. Experiments on a Mongolian dataset with 325 hours of training show that the pronunciation lexicon based on the acoustic subword modeling unit can effectively construct the WFST-based Mongolian ASR system. Further, removing the control characters when building the acoustic subword modeling unit can further improve the ASR system performance.

Perbandingan Fonologi Bahasa Indonesia dan Bahasa Thailand

Research aims to explore the comparison between Indonesian and Thai languages, focusing on structure, phonology, and grammar rules. The methodology involves historical and cultural analysis to understand their influences. Phonetic examination compares pronunciation of phonemes. The study also investigates morphological aspects like word formation, inflection, and affixation. Syntactic analysis examines sentence structures, grammar, and phrase roles. The findings provide a comprehensive overview of linguistic differences, alongside cultural and historical impacts. Implications include contributions to interlanguage understanding, development of teaching methods, and cross-cultural translation. This research serves as a foundation for linguistic practitioners, translators, and language instructors to comprehend and leverage the differences and similarities between both languages.

THE COMPARISON OF PHONEME PRONUNCIATION PHONOLOGICAL INTERFERENCE OF ق [Q] IN SUNDANESE AND JAVANESE SPEAKERS

The study analyses the phonological interference of phoneme pronunciation [q] by Sundanese and Javanese speakers compared with Arabic speakers. The researchers used a descriptive comparative qualitative method. The researchers listened, took notes, and documented the voice recordings of male students from the Sundanese and Javanese ethnic groups when reading the Qur'an to collect research data and analyse it using Praat software version 6.2.13. From the results of the recordings, Sundanese-speaking students and Javanese speakers would then be compared with reference speakers, namely Syekh Sa'ad Al-ghamidi, who has the capability of reciting verses from the Qur'an. The research results indicated phonological interference in the phoneme [q] when Sundanese and Javanese male students read the Qur'an. The factor contributing to the occurrence of pronunciation errors in the phoneme [q] was the transfer of SL and JL elements when using AL. Errors occurred due to deviations from the proper language rules. The research is expected to have positive implications, minimizing interference and increasing fluency in reading the Qur'an.

A RTL Implementation of Heterogeneous Machine Learning Network for French Computer Assisted Pronunciation Training

Computer-assisted pronunciation training (CAPT) is a helpful method for self-directed or long-distance foreign language learning. It greatly benefits from the progress, and of acoustic signal processing and artificial intelligence techniques. However, in real-life applications, embedded solutions are usually desired. This paper conceives a register-transfer level (RTL) core to facilitate the pronunciation diagnostic tasks by suppressing the mulitcollinearity of the speech waveforms. A recently proposed heterogeneous machine learning framework is selected as the French phoneme pronunciation diagnostic algorithm. This RTL core is implemented and optimized within a very-high-level synthesis method for fast prototyping. An original French phoneme data set containing 4830 samples is used for the evaluation experiments. The experiment results demonstrate that the proposed implementation reduces the diagnostic error rate by 0.79–1.33% compared to the state-of-the-art and achieves a speedup of 10.89× relative to its CPU implementation at the same abstract level of programming languages.

Extracting Spatial Muscle Activation Patterns in Facial and Neck Muscles for Silent Speech Recognition Using High-Density sEMG

Human is capable of producing versatile and precise speech owing to the complex neuromuscular systems that control the movement of facial and neck muscle groups. Silent speech recognition based on surface electromyography (sEMG) has been widely researched, which enables speech communication in the absence of an audible acoustic signal. However, the differences in the activities of facial and neck muscle groups under different phoneme pronunciation conditions have not been well investigated. The present work aims to quantify the macroscopic spatial patterns of the muscle activations at the phoneme level during the movement of facial and neck speech articulator muscle groups (SAMGs) using 320-channel high-density (HD) electrode arrays. 10 subjects performed 14 vowel speech tasks and 15 consonant speech tasks in audible speech and silent speech modes, respectively. Specifically, the root-mean-square heat maps of sEMG signals were used to characterize the SAMGs activation patterns. The results show that the muscle activation regions are overlapped under different phoneme pronunciation conditions and symmetric between the left and right sides of face and neck with the average correlation coefficient > 0.9. In addition, the best result can achieve a classification accuracy of 85.78% for consonant and 79.42% for vowel in silent speech mode. The overall results can provide a reference to the hardware or electrode design of portable device for practical use in future work.

IMPLEMENTASI METODE FONETIK DALAM PEMBELAJARAN MAHARAH ISTIMA’ DAN KALAM UNTUK PEBELAJAR TINGKAT PEMULA

One of the problems of students' lack of kalam and qiroah abilities is the lack of teaching methods used by coaches. For example, many teachers still use the lecture method in teaching any maharah. Therefore, it is necessary to clarify and provide alternative learning methods, especially for maharah kalam and istima`. This study aims to find out the steps for using the phonetic method in learning maharah istima' and kalam for beginners. This research is a library research using a qualitative descriptive approach. With the Maharah Kalam and Istima` learning phonetic methods for the beginner level, the focus is on repetition and pronunciation of phonemes properly and correctly. The phonetic method begins with practicing distinguishing Arabic phonemes, namely hijaiyah letters, then continues with distinguishing words or mufrodat that are similar in pronunciation. Pronunciation with good makhraj and intonation needs to be continuously guarded so that learning has a high success rate.

Event-Related Potential Correlates of Learning to Produce Novel Foreign Phonemes.

Learning to pronounce a foreign phoneme requires an individual to acquire a motor program that enables the reproduction of the new acoustic target sound. This process is largely based on the use of auditory feedback to detect pronunciation errors to adjust vocalization. While early auditory evoked neural activity underlies automatic detection and adaptation to vocalization errors, little is known about the neural correlates of acquiring novel speech targets. To investigate the neural processes that mediate the learning of foreign phoneme pronunciation, we recorded event-related potentials when participants (N = 19) pronounced native or foreign phonemes. Behavioral results indicated that the participants' pronunciation of the foreign phoneme improved during the experiment. Early auditory responses (N1 and P2 waves, approximately 85-290 ms after the sound onset) revealed no differences between foreign and native phonemes. In contrast, the amplitude of the frontocentrally distributed late slow wave (LSW, 320-440 ms) was modulated by the pronunciation of the foreign phonemes, and the effect changed during the experiment, paralleling the improvement in pronunciation. These results suggest that the LSW may reflect higher-order monitoring processes that signal successful pronunciation and help learn novel phonemes.

AN IN-DEPTH ANALYSIS ON ERRORS OF INDONESIAN STUDENTS WITH LOW PROFICIENT LEVEL IN PRONOUNCING CONSONANT ENGLISH PHONEMES

This study aims to determine the pronunciation errors of phonemes produced by students of Muhammadiyah University of Bulukumba. This study applies a qualitative paradigm. The subjects of this study were second semester students of the Department of English Education with low proficiency levels at the University of Muhammadiyah Bulukumba. Students were asked to pronounce 24 consonant sounds at the beginning, middle, and end of the word to find the data. Students' pronunciation was then phonologically analyzed based on the classification of pronunciation errors found by Moulton, W. G. (1962).The results shows that errors in the pronunciation of English consonants are found in stop voicess, fricative, africative, nasal and semi-vowel consonants. The types of errors in the pronunciation of consonant phonemes are allophones, phonemics and phonetics errors. Allophone error is found in the stop voiceless consonants /p/, /t/, and /k/ which were found at the beginning of the syllable. Phonemic and phonetic errors are found in the pronunciation of voiced /v/, voiced /z/, voiceless /θ/, voiced /ð/, and voiceless /ʃ/. The same error is also found in voiceless /ʃ/ which was pronounced without using the sound /h/, as well as phonetic errors that was pronounced using the sound /t/. Phonetic errors are also found in the pronunciation of fricative sounds /tʃ/ and /dᴣ/, nasal /ƞ/ and semi vowel sounds /j/ which were pronounced not in accordance with the actual sound, thus other meaningless sounds are formed.

An English Pronunciation Quality Evaluation Model Based on Multi-dimensional Features

As a universal language, English has been paid more and more attention, among which oral English learning is very important. In this paper, the two key technologies of pronunciation error detection and quality evaluation are studied, both of them are effectively integrated, aiming to build a model for L2 learners’ English pronunciation quality evaluation. This paper mainly studies two different methods of pronunciation error detection. Based on the speech recognition framework, the standard score is compared with the threshold to judge the correctness of phoneme pronunciation, and the phoneme-dependent threshold is set to improve the maximum Precision to 0.44. By judging the correct pronunciation and confusing phoneme, the accuracy of pronunciation error detection is improved to 81.26%. This paper proposes the fusion algorithm from multi-dimensions of speech fluency and intonation respectively, and a newly designed feature called word duration ratio, which significantly improve the correlation of pronunciation quality evaluation to 0.746.

Native language identification for Indian-speakers by an ensemble of phoneme-specific, and text-independent convolutions

Identifying the social background of an unknown speaker by speech accent has multiple applications including in forensic profiling and adaptation of speech recognition. The most effective accent classification models based on phoneme pronunciation require the presence of certain phonemes in the test speech and hence, are applicable only for a longer duration of test samples. On the other hand, the text-independent classifiers disregard the phoneme and linguistic information completely. This paper proposes an ensemble of convolutional neural networks for phoneme-based short-term and text-independent long-term classification of speech regarding speaker background profiling. The model is evaluated by classifying the native language of Indian speakers by their English speech. Both the classifiers within the ensemble complement each other positively; to give higher classification accuracy as compared to classification accuracies obtained from the individual classifiers. Low-pass filtering based speech augmentation has been proven to further improve the classification performance and average accuracy, with up to 79% and 73.7% accuracies achieved for speaker-level and sentence-level tests, respectively.