Phonemic Variation Research Articles

REDUPLIKASI BAHASA MELAYU JAMBI DI DESA KARMEO KECAMATAN BATIN XXIV KABUPATEN BATANGHARI

Reduplication of Jambi Malay in Karmeo Village, Batin XXIV District, Batanghari Regency. The purpose of this study is to describe the use of reduplication or repeated words in Karmeo Village from three aspects: form, process, and meaning. The data in this study are in the form of informants' utterances, which can be found in Karme Village. The method used in this research is descriptive qualitative. Data collection techniques used recording techniques, involvement-free listening techniques, and note-taking techniques. The data analysis technique uses the Miles and Huberman model technique. The results of this study found that there were four forms of reduplication: (1) whole reduplication, (2) partial reduplication, (3) incremental reduplication, and (4) reduplication with phoneme variations. This study also found five reduplication processes and 10 reduplication meanings in Jambi Malay in Karmeo Village, Batin XXIV District, Batanghari Regency.

Intercultural sensitivity in the online multicultural community: Indonesian students’ experiences

The online social network facilitates excessive opportunities for intercultural communication in the online multicultural community. Occasionally, intercultural communication possibly results in misunderstanding, confusion, or stereotypes. Intercultural sensitivity, therefore, is necessary to respond to probable issues in online intercultural communication based on Indonesian students’ experiences. This narrative study portrayed the development of intercultural sensitivity in the online multicultural setting. The data were compiled from narrative reports and recordings and then analyzed based on Hunter’s analysis of the narrative. It reveals the development of intercultural sensitivity in the themes of 1) embracing a phonemic variation of English, 2) developing mutual strategies to understand the language, 3) celebrating small similarities, and 4) self-control to listen respectfully. The findings are primarily meant for intercultural education. The study also suggests the use of an authentic intercultural setting for English language learners to develop their intercultural sensitivity and bring the discussions after practice in the classroom.

Phonemic Variations in English Loanwords Borrowed into Afghani Pashto

Due to Pashto-English language contact, a large number of words are borrowed from English into Afghani Pashto. These English loanwords undergo certain types of variation. This study aims at phonemic variations in English loanwords borrowed into Afghani Pashto. A corpus of 90 English loanwords was collected from ten audio-video programs from Afghani TV channels. They were news and discussion panels. The conversation of Afghani Pashto speakers was observed for the collection of the corpus. The data were analyzed according to Levenshtein's (1966) Distance Algorithm Model to investigate the phonemic variations in English loanwords. Based on the research findings, it was revealed that most of the loanwords undergo phonemic variation and some words are just adopted. Most of the words undergo a change of vowel sounds. Some of the words also maintain a change in the consonant sounds. In some cases, the words experience a change in both the vowel and consonant sounds.

Analisis Kontrastif Dialek Gayo Uken dan Toa

The differences between the Uken and Toa dialects are easily identified in the speakers' daily conversations. Speakers of these two dialects are still easy to understand each other. The difference occurs in some vowel sounds, intonation, some vocabulary, and some greetings in everyday dialogue. The objectives to be achieved in this study are to determine the differences between the Gayo Uken and Toa dialects. The research method used is a qualitative research method, this research was conducted using a descriptive analysis method. The difference in conversation between Uken and Toa people can be seen in terms of their pronunciation in interacting. The difference between the two Gayo dialects is in the form of words that are similar between the gayo uken and toa dialects but have the same meaning and what distinguishes these two dialects is the way they are pronounced. And based on the pronunciation of the Uken and Toa dialects spoken by the community, there are many phoneme variations, each word conveyed produces differences in vowel phonemes and consonant phonemes.

Pronunciation Difficulties and Challenges in the Field of Research in Jordan

This paper presents the current evidence regarding English pronunciation challenges and problems in the Jordanian context by reviewing related literature. The present study was carried out to determine the extent to which pronunciation research regarding challenges is carried out in Jordan. The available literature on pronunciation is limited to phonological variances, consonants’ and vowels’ variations, syllable structure differences and similarities, and mother tongue as an effective element. In order to achieve this objective identifying pronunciation difficulties and challenges in the field of research in Jordan, this study followed systematic literature review (SLR) framework adapted from previous studies in this regard. The findings revealed that one of the key problems in learning English language pronunciation is related to the socio-psycho variables e.g., anxiety, self-confidence, motivation, attitudes and so on noting that studies investigated did not present these factors in detail and they focused more on the role of learners’ native language, exposure to language, phonemic variations and vowels, and syllable structures’ differences. Hence, future studies in this regard are recommended to investigate the challenges related to anxiety, self-confidence, motivation, attitudes and their role on affecting pronunciation teaching and learning.

Extracting hierarchical features of cultural variation using network-based clustering.

High-dimensional datasets on cultural characters contribute to uncovering insights about factors that influence cultural evolution. Because cultural variation in part reflects descent processes with a hierarchical structure – including the descent of populations and vertical transmission of cultural traits – methods designed for hierarchically structured data have potential to find applications in the analysis of cultural variation. We adapt a network-based hierarchical clustering method for use in analysing cultural variation. Given a set of entities, the method constructs a similarity network, hierarchically depicting community structure among them. We illustrate the approach using four datasets: pronunciation variation in the US mid-Atlantic region, folklore variation in worldwide cultures, phonemic variation across worldwide languages and temporal variation in first names in the US. In these examples, the method provides insights into processes that affect cultural variation, uncovering geographic and other influences on observed patterns and cultural characters that make important contributions to them.

Human and machine learning expertise applied to speech and music signals processing

Machine learning techniques provide extensive advances in automated signal and data processing in many fields. One of the critical areas in acoustic signal processing is extracting relevant information from the speech signal. The speech signal contains phonemic variation, temporal structure, prosody, timbre, and voice quality. It also includes various aspects of the speaker’s profile, such as emotions or sentiments. What is easily discerned and analyzed by a human escapes a machine learning-based approach when dealing with all that complexity at once. One may easily see parallels to musical signal processing as such aspects as temporal structure, timbre, and music quality, etc., are of importance, especially as music also carries an artistic message. An example of such a functional concept of music is when it is associated with the image, as the ultimate goal of a film music composer is to enhance or evoke the audience’s emotions. A question appears whether a machine learning technique may discern emotions associated with a given scene the same way as the human does. This paper focuses on the application of machine learning to speech and music signal analyses. It also emphasizes speech and music signal processing covering recent advances in deep learning.

Phonemic Variation of Urdu Loanwords in Standard British English

Borrowing is one of the most common sources of new words in languages and loanwords are one of the types of borrowings across language boundaries. The English language has adopted many words from other languages throughout history. In this process, loanwords sometimes undergo certain variations. The present study is about phonemic variation of Urdu loanwords in standard British English. A list of 135 Urdu words of daily use that English has borrowed was compiled purposively from Oxford Advanced Learners’ Dictionary, 10th ed. (2020) for analysis. All words in the list were transcribed into IPA. Urdu pronunciation was checked from ("Urdu Lughat," 2017) and Farhang-e-Talaffuz (2017) whereas English pronunciation was checked from Oxford Advanced Learners’ Dictionary 10th ed. (2020) and a comparison was made to highlight phonemic variation. Analysis of data indicates that almost one third loanwords retain their pronunciation and among the rest, the majority of words undergo change of vowel sounds whereas others either change a consonant sound or vowel and consonant both in some cases.

Phonemic Training Modulates Early Speech Processing in Pre-reading Children.

Phonemic awareness and rudimentary grapheme knowledge concurrently develop in pre-school age. In a training study, we tried to disentangle the role of both precursor functions of reading for spoken word recognition. Two groups of children exercised with phonemic materials, but only one of both groups learnt corresponding letters to trained phonemes. A control group exercised finger-number associations (non-linguistic training). After the training, we tested how sensitive children were to prime-target variation in word onset priming. A group of young adults took part in the same experiment to provide data from experienced readers. While decision latencies to the targets suggested fine-grained spoken word processing in all groups, event-related potentials (ERPs) indicated that both phonemic training groups processed phonemic variation in more detail than the non-linguistic training group and young adults at early stages of speech processing. Our results indicate temporal plasticity of implicit speech processing in pre-school age as a function of explicit phonemic training.

Phonemic variations in similar words of Turkish and Urdu language

Urdu language is a member of Indo-European family tree and within the zone of Indo-Iranian branch, whereas Turkish language is a member of Altaic family tree. Both of these languages belong to different family trees, but these languages have many words in common. Urdu language has 41 consonant sounds and 11 vowel sounds, whereas Turkish language has 21 consonant sounds and 8 vowel sounds. Both of these languages don’t have same number of phonemes. It is interesting to contemplate, how the speakers of both languages produce and perceive these common words in their languages. Therefore, a study is designed to explore the phonemic variations in similar words of Urdu and Turkish languages. In order to find out variations, a list of 75 words, which are common in Urdu and Turkish language was prepared in form of text. The data of this study was collected from Pak-Turk school of Lahore. The prepared words list was given to 10 Turkish speakers. These speakers were selected by using purposive sampling technique. The audios of the speakers were recorded and transcribed into standard Turkish IPA symbols. Levenshtein algorithm framework is used to draw a comparative analysis of Turkish phonemic transcriptions with standard Urdu phonemic transcriptions of the same words. With the help of Levenshtein algorithm, phonemic variations in similar words of both languages was measured. The intriguing result of the study will help in establishing the understanding about the production directions of the similar words used by the speakers of both languages.

Phonemic variations in similar words of Turkish and Urdu language

Phonemic variations in similar words of Turkish and Urdu language

Effect of phoneme variations on blind reverberation time estimation

This study focuses on an unexplored aspect of the performance of algorithms for blind reverberation time (T) estimation – on the effect that speech signal’s phonetic content has on the value of the estimate ofTthat is obtained from the reverberant version of that signal. To this end, the performance of three algorithms is assessed on a set of logatome recordings artificially reverberated with room impulse responses from four rooms, with theirT20value in the [0.18, 0.55] s interval. Analyses of variance showed that the null hypotheses of equal means of estimation errors can be rejected at the significance level of 0.05 for the interaction terms between the factors “vowel”, “consonant”, and “room”, while the results of Tukey’s multiple comparison procedure revealed that there are both some similarities in the behaviour of the algorithms and some differences, where the latter are stemming from the differences in the details of algorithms’ implementation such as the number of frequency bands and whetherTis estimated continuously or only on the selected, the so-called speech decay, segments of the signal.

Worldwide distribution of the DCDC2 READ1 regulatory element and its relationship with phoneme variation across languages

DCDC2 is a gene strongly associated with components of the phonological processing system in animal models and in multiple independent studies of populations and languages. We propose that it may also influence population-level variation in language component usage. To test this hypothesis, we investigated the evolution and worldwide distribution of the READ1 regulatory element within DCDC2, and compared its distribution with variation in different language properties. The mutational history of READ1 was estimated by examining primate and archaic hominin sequences. This identified duplication and expansion events, which created a large number of polymorphic alleles based on internal repeat units (RU1 and RU2). Association of READ1 alleles was studied with respect to the numbers of consonants and vowels for languages in 43 human populations distributed across five continents. Using population-based approaches with multivariate ANCOVA and linear mixed effects analyses, we found that the RU1-1 allele group of READ1 is significantly associated with the number of consonants within languages independent of genetic relatedness, geographic proximity, and language family. We propose that allelic variation in READ1 helped create a subtle cognitive bias that was amplified by cultural transmission, and ultimately shaped consonant use by different populations over time.

Teaching elements of English RP connected speech and CALL: Phonemic assimilation

Phonology represents an important part of the English language; however, in the course of English language acquisition, it is rarely treated with proper attention. Connected speech is one of the aspects essential for successful communication, which comprises effective auditory perception and speech production. In this paper I explored phonemic assimilation, which results in successive sounds at word boundaries influencing each other, as an element of connected speech, and studied how teaching it can be supported with computer-assisted language learning. The research conducted revealed that elements of phonemic assimilation are found frequently in all styles of speech and thus, the knowledge of it is necessary for developing proper listening and speaking skills. I advocate the use of the Internet as one of the best possible resources of listening materials for learning phonemic variations and show how various web tools and means of technology can be used for preparing, presenting and storing educational materials. I believe this article makes a contribution to the corpus of research and instruction on connected speech as part of the standard British accent and will have an impact in raising general awareness of its significant role in the process of English language learning.

Pronunciation Variants Prediction Method to Detect Mispronunciations by Korean Learners of English

This article presents an approach to nonnative pronunciation variants modeling and prediction. The pronunciation variants prediction method was developed by generalized transformation-based error-driven learning (GTBL). The modified goodness of pronunciation (GOP) score was applied to effective mispronunciation detection using logistic regression machine learning under the pronunciation variants prediction. English-read speech data uttered by Korean-speaking learners of English were collected, then pronunciation variation knowledge was extracted from the differences between the canonical phonemes and the actual phonemes of the speech data. With this knowledge, an error-driven learning approach was designed that automatically learns phoneme variation rules from phoneme-level transcriptions. The learned rules generate an extended recognition network to detect mispronunciations. Three different mispronunciation detection methods were tested including our logistic regression machine learning method with modified GOP scores and mispronunciation preference features; all three methods yielded significant improvement in predictions of pronunciation variants, and our logistic regression method showed the best performance.

An Experimental Analysis on Integrating Multi-Stream Spectro-Temporal, Cepstral and Pitch Information for Mandarin Speech Recognition

Gabor features have been proposed for extracting spectro-temporal modulation information from speech signals, and have been shown to yield large improvements in recognition accuracy. We use a flexible Tandem system framework that integrates multi-stream information including Gabor, MFCC, and pitch features in various ways, by modeling either or both of the tone and phoneme variations in Mandarin speech recognition. We use either phonemes or tonal phonemes (tonemes) as either the target classes of MLP posterior estimation and/or the acoustic units of HMM recognition. The experiments yield a comprehensive analysis on the contributions to recognition accuracy made by either of the feature sets. We discuss their complementarities in tone, phoneme, and toneme classification. We show that Gabor features are better for recognition of vowels and unvoiced consonants, while MFCCs are better for voiced consonants. Also, Gabor features are capable of capturing changes in signals across time and frequency bands caused by Mandarin tone patterns, while pitch features further offer extra tonal information. This explains why the integration of Gabor, MFCC, and pitch features offers such significant improvements.

Effects of phonemic variability and language dominance on Canadian French-English bilinguals’ perception of French vowels in various phonological contexts

Phonemic variability can cause productions within a category of one language to be mapped onto multiple categories in a different language [Escudero (2009)]. For example, French productions of /ε/ are labeled both as /e/ and /ε/ by monolingual English listeners, likely due to spectral variability in production of French /ε/ [Strange et al. (2009)]. The present study examines how phonemic variability in Canadian French (CF) affects the perceptual tendencies of bilingual Canadian French-English listeners with varying levels of language dominance. Vowel productions by monolingual CF speakers were used in a modified identification task [Law (2011)]. The vowels were word-final in several phonological contexts (preceded by labial, coronal, and back consonants; followed by labial and coronal consonants) in real and nonwords embedded in carrier phrases. A subset of these vowels /e-ε-i-y/ was analyzed for duration, vocalic midpoint, and formant trajectories to examine the relationship between phonemic variation (as a function of phonological environment) and the perception results by bilinguals dominant in either CF or Canadian English (CE). We predict that the performance of CE-dominant listeners will vary in terms of speed and accuracy based on how similar each token is to CE vowel category expectations.

A Study on the Methods of Pronunciation Education in Korean Language Textbooks for Foreign Beginners - Focused on the Rules of Phonemic Variation

A Study on the Methods of Pronunciation Education in Korean Language Textbooks for Foreign Beginners - Focused on the Rules of Phonemic Variation

Estimating speech spectra for copy synthesis by linear prediction and by hand

Linear prediction is a widely available technique for analyzing acoustic properties of speech, although this method is known to be error-prone. New tests assessed the adequacy of linear prediction estimates by using this method to derive synthesis parameters and testing the intelligibility of the synthetic speech that results. Matched sets of sine-wave sentences were created, one set using uncorrected linear prediction estimates of natural sentences, the other using estimates made by hand. Phoneme restrictions imposed on linguistic properties allowed comparisons between continuous and intermittent voicing, oral or nasal and fricative manner, and unrestricted phonemic variation. Intelligibility tests revealed uniformly good performance with sentences created by hand-estimation and a minimal decrease in intelligibility with estimation by linear prediction due to manner variation with continuous voicing. Poorer performance was observed when linear prediction estimates were used to produce synthetic versions of phonemically unrestricted sentences, but no similar decline was observed with synthetic sentences produced by hand estimation. The results show a substantial intelligibility cost of reliance on uncorrected linear prediction estimates when phonemic variation approaches natural incidence.

Emotional Feature Extraction Method Based on the Concentration of Phoneme Influence for Human–Robot Interaction

Depending on the emotion of speech, the meaning of the speech or the intention of the speaker differs. Therefore, speech emotion recognition, as well as automatic speech recognition is necessary to communicate precisely between humans and robots for human–robot interaction. In this paper, a novel feature extraction method is proposed for speech emotion recognition using separation of phoneme class. In feature extraction, the signal variation caused by different sentences usually overrides the emotion variation and it lowers the performance of emotion recognition. However, as the proposed method extracts features from speech in parts that correspond to limited ranges of the center of gravity of the spectrum (CoG) and formant frequencies, the effects of phoneme variation on features are reduced. Corresponding to the range of CoG, the obstruent sounds are discriminated from sonorant sounds. Moreover, the sonorant sounds are categorized into four classes by the resonance characteristics revealed by formant frequency. The result shows that the proposed method using 30 different speakers' corpora improves emotion recognition accuracy compared with other methods by 99% significance level. Furthermore, the proposed method was applied to extract several features including prosodic and phonetic features, and was implemented on 'Mung' robots as an emotion recognizer of users.