Phonemic Features Research Articles

Ease of Articulation in Selecting First Names

Investigating the ease of articulation in selecting names contributes to emphasizing the effect of articulatory features on speech perception and production. It explains why some names are distinctive among multi-semantic references and sociocultural dominance and are more likely appropriate for one sex than the other. This study explores how distinctive features of phonemes subconsciously derive our conception of first names, regardless of rich meanings or morphological endings. It argues that all selected names demonstrate articulatory-easy phonemes. Sex distinction is assumed to attribute phonemes of easier configurations to characterize girl names more than boy names. By inspecting a sample that exhibited no feminine suffixes or compounding structures, the study analyzed the phonemic components based on the articulatory-ease module. The findings justified equivalent preference of boy names and girl names to the ease of bilabial, coronal, voiceless features, and disyllabic structures. However, interest in names with easier anterior coronal sonorants, posterior distributed strident and glottal dominated the selected girl names in line with sound symbolism. Moreover, prominent everlasting names indicated a mechanism of articulatory ease that identified one essential unit to develop a unique smooth flow of phonemes. The distinctive characteristic features of this unit marked sex distinction and determined popularity. KEYWORDS articulatory mechanism, articulatory module, conserving effort, distinctive features, prominent names, sound symbolism

Transcranial Magnetic Stimulation Facilitates Neural Speech Decoding.

Transcranial magnetic stimulation (TMS) has been widely used to study the mechanisms that underlie motor output. Yet, the extent to which TMS acts upon the cortical neurons implicated in volitional motor commands and the focal limitations of TMS remain subject to debate. Previous research links TMS to improved subject performance in behavioral tasks, including a bias in phoneme discrimination. Our study replicates this result, which implies a causal relationship between electro-magnetic stimulation and psychomotor activity, and tests whether TMS-facilitated psychomotor activity recorded via electroencephalography (EEG) may thus serve as a superior input for neural decoding. First, we illustrate that site-specific TMS elicits a double dissociation in discrimination ability for two phoneme categories. Next, we perform a classification analysis on the EEG signals recorded during TMS and find a dissociation between the stimulation site and decoding accuracy that parallels the behavioral results. We observe weak to moderate evidence for the alternative hypothesis in a Bayesian analysis of group means, with more robust results upon stimulation to a brain region governing multiple phoneme features. Overall, task accuracy was a significant predictor of decoding accuracy for phoneme categories (F(1,135) = 11.51, p < 0.0009) and individual phonemes (F(1,119) = 13.56, p < 0.0003), providing new evidence for a causal link between TMS, neural function, and behavior.

Auditory cortex encodes lipreading information through spatially distributed activity

Watching a speaker's face improves speech perception accuracy. This benefit is enabled, in part, by implicit lipreading abilities present in the general population. While it is established that lipreading can alter the perception of a heard word, it is unknown how these visual signals are represented in the auditory system or how they interact with auditory speech representations. One influential, but untested, hypothesis is that visual speech modulates the population-coded representations of phonetic and phonemic features in the auditory system. This model is largely supported by data showing that silent lipreading evokes activity in the auditory cortex, but these activations could alternatively reflect general effects of arousal or attention or the encoding of non-linguistic features such as visual timing information. This gap limits our understanding of how vision supports speech perception. To test the hypothesis that the auditory system encodes visual speech information, we acquired functional magnetic resonance imaging (fMRI) data from healthy adults and intracranial recordings from electrodes implanted in patients with epilepsy during auditory and visual speech perception tasks. Across both datasets, linear classifiers successfully decoded the identity of silently lipread words using the spatial pattern of auditory cortex responses. Examining the time course of classification using intracranial recordings, lipread words were classified at earlier time points relative to heard words, suggesting a predictive mechanism for facilitating speech. These results support a model in which the auditory system combines the joint neural distributions evoked by heard and lipread words to generate a more precise estimate of what was said.

Effect of Abstract Phonemic Complexity on Mismatch Negativity (MMN) Amplitude.

Mismatch negativity (MMN) reflects a pre-perceptual neurophysiological response that is generated subconsciously due to the interruption of a memory trace of ongoing sensory events in the environment. It has been widely used by researchers to understand complex perceptual mechanisms. Furthermore, it has been recommended as an objective tool to investigate disorders related to auditory cognition in hearing aid and cochlear implant users. Many researchers suggest that utilizing a larger acoustic difference between standard and deviant stimuli within the oddball paradigm will lead to a more robust MMN response. The purpose of the present study is to examine if increasing the abstract phonemic contrast between standard and deviant stimuli in the oddball paradigm leads to a more robust MMN response. Fourteen young female adults participated in the present study. To ensure that the MMN response was elicited by phonemic and not acoustic differences in the stimuli, a one-to-many ratio was created for the abstract phonemic features while controlling the acoustic features when designing the oddball paradigm as described by Phillips et al. (2000). MMN amplitude was measured at the Cz and Fz electrodes in 2 conditions, with 2 trials in each condition. In condition 1, the standards and deviants differed by one distinctive feature: voicing in trial 1 (/tӕ/ was standard and /dӕ/ was deviant), and place of articulation in trial 2 (/bӕ/ was standard and /dӕ/ was deviant). In condition 2, the standards and deviants differed across two distinctive features: voicing and place of articulation. In trial 1, /pӕ/ was standard and /dӕ/ was deviant; in trial 2, /dӕ/ was standard and /pӕ/ was deviant. MMN amplitudes elicited by two distinctive features were significantly larger than MMN amplitudes elicited by one distinctive feature (p<0.001). Trials 1 and 2 in each condition showed no statistical difference, and they were repeatable and highly correlated. Recordings from the Cz and Fz electrodes showed no statistical difference and were highly correlated and similar in morphology. It is known in the literature that increasing acoustic complexity elicits a more robust MMN. The present study showed that this assumption can be extended to abstract phonemic complexity. Increasing the phonemic complexity by utilizing more distinctive features in the oddball paradigm increased the amplitude and robustness of the MMN.

Learning Deep Embedding with Acoustic and Phoneme Features for Speaker Recognition in FM Broadcasting

Text-independent speaker verification (TI-SV) is a crucial task in speaker recognition, as it involves verifying an individual’s claimed identity from speech of arbitrary content without any human intervention. The target for TI-SV is to design a discriminative network to learn deep speaker embedding for speaker idiosyncrasy. In this paper, we propose a deep speaker embedding learning approach of a hybrid deep neural network (DNN) for TI-SV in FM broadcasting. Not only acoustic features are utilized, but also phoneme features are introduced as prior knowledge to collectively learn deep speaker embedding. The hybrid DNN consists of a convolutional neural network architecture for generating acoustic features and a multilayer perceptron architecture for extracting phoneme features sequentially, which represent significant pronunciation attributes. The extracted acoustic and phoneme features are concatenated to form deep embedding descriptors for speaker identity. The hybrid DNN demonstrates not only the complementarity between acoustic and phoneme features but also the temporality of phoneme features in a sequence. Our experiments show that the hybrid DNN outperforms existing methods and delivers a remarkable performance in FM broadcasting TI-SV.

Detecting Forged Audio Files Using "Mixed Paste" Command: A Deep Learning Approach Based on Korean Phonemic Features.

The ubiquity of smartphones today enables the widespread utilization of voice recording for diverse purposes. Consequently, the submission of voice recordings as digital evidence in legal proceedings has notably increased, alongside a rise in allegations of recording file forgery. This trend highlights the growing significance of audio file authentication. This study aims to develop a deep learning methodology capable of identifying forged files, particularly those altered using "Mixed Paste" commands, a technique not previously addressed. The proposed deep learning framework is a composite model, integrating a convolutional neural network and a long short-term memory model. It is designed based on the extraction of features from spectrograms and sequences of Korean consonant types. The training of this model utilizes an authentic dataset of forged audio recordings created on an iPhone, modified via "Mixed Paste", and encoded. This hybrid model demonstrates a high accuracy rate of 97.5%. To validate the model's efficacy, tests were conducted using various manipulated audio files. The findings reveal that the model's effectiveness is not contingent on the smartphone model or the audio editing software employed. We anticipate that this research will advance the field of audio forensics through a novel hybrid model approach.

Transliteration Characteristics in Romanized Assamese Language Social Media Text and Machine Transliteration

This article aims to understand different transliteration behaviors of Romanized Assamese text on social media. Assamese, a language that belongs to the Indo-Aryan language family, is also among the 22 scheduled languages in India. With the increasing popularity of social media in India and also the common use of the English Qwerty keyboard, Indian users on social media express themselves in their native languages, but using the Roman/Latin script. Unlike some other popular South Asian languages (say Pinyin for Chinese), Indian languages do not have a common standard romanization convention for writing on social media platforms. Assamese and English are two very different orthographical languages. Thus, considering both orthographic and phonemic characteristics of the language, this study tries to explain how Assamese vowels, vowel diacritics, and consonants are represented in Roman transliterated form. From a dataset of romanized Assamese social media texts collected from three popular social media sites: (Facebook, YouTube, and X (formerly known as Twitter)), 1 we have manually labeled them with their native Assamese script. A comparison analysis is also carried out between the transliterated Assamese social media texts with six different Assamese romanization schemes that reflect how Assamese users on social media do not adhere to any fixed romanization scheme. We have built three separate character-level transliteration models from our dataset. One using a traditional phrase-based statistical machine transliteration model, (1) PBSMT model and two separate neural transliteration models, (2) BiLSTM neural seq2seq model with attention, and (3) Neural transformer model. A thorough error analysis has been performed on the transliteration result obtained from the three state-of-the-art models mentioned above. This may help to build a more robust machine transliteration system for the Assamese social media domain in the future. Finally, an attention analysis experiment is also carried out with the help of attention weight scores taken from the character-level BiLSTM neural seq2seq transliteration model built from our dataset.

SEGMENTAL UNITS AS AN INDICATOR OF EMOTIONAL PERCEPTION OF THE SOUNDING SPEECH

Problem Statement. Our research refers to the theory of emotionality and focuses on expressive means of sounding speech. When considering expressive means of sounding speech, we refer to the segmental level, namely to the distinctive features of vowel phonemes. We suggest that the complexes of semantic features of vowel phonemes and the frequency of their realization is one of the indicators of emotional perception and its correlation with a particular emotion. Besides it affects the perception of sounding speech in a particular emotional way, regardless of its semantics. The author has described and developed the mechanism of psycholinguistic experiment on revealing the perception of emotionally colored speech in connection with the functioning of the units of the language system in their differential attributes in the perception of sounding speech. Results.The results of the research showed that the distinctive features of vowel phonemes may be one of the indicators of correlation of emotional perception with a particular emotion. Moreover, when emotionality increases, one should expect regular and systematic changes of means for its expression. Furthermore, the polar basic emotions of both German and Russian speakers may coincide in the perception of the same material, which can be explained by the presence of universal and culture-bound specifics of emotional perception of the distinctive characteristics of vowels. Conclusion. Due to the results of the research, we may conclude that a systematic approach to the study of the phenomena of speech is possible because of the fact that the relationship between the functioning of the distinctive characteristics of vowel phonemes and the emotional coloring of the sounding text has a systemic nature.

Phonemic Feature Scoring as a Tool for Progress Monitoring During Language Interventions for Children With Autism and Minimal Verbal Skills.

An increasing number of studies focus on verbal treatments for children with autism and minimal verbal skills. However, clinical tools for progress monitoring during interventions are lacking. The aim of this clinical focus article is to provide illustrations on the utility of a phonemic feature scoring system as a progress-monitoring tool, focusing on benefits and limitations as well as indications for use. Current practices for progress monitoring during language interventions with children with autism and minimal verbal skills are reviewed. A phonemic feature scoring tool is provided to aid clinicians in assessing the accuracy and consistency of expressive word productions. The authors illustrate the use of phonemic feature scoring as a progress-monitoring tool for two children with autism and minimal verbal skills, contrasting the phonemic feature scoring system to correct/incorrect, phoneme-level, and whole-word scoring. Case 1 demonstrates a scenario where the child's speech intelligibility is low and clinical use of the phonemic feature scoring system captures discrete changes in speech production progress not represented by correct/incorrect, phoneme-level, and whole-word scoring. However, Case 2 represents a situation where once a child's speech intelligibility improves, the phonemic feature scoring system is no longer needed, and correct/incorrect scoring may be sufficient. The phonemic feature scoring system allows clinicians to track phonemic feature changes in word productions and provides detailed progress monitoring information, leading to adaptations of the intervention for each individual.

Phonological acquisition depends on the timing of speech sounds: Deconvolution EEG modeling across the first five years

The late development of fast brain activity in infancy restricts initial processing abilities to slow information. Nevertheless, infants acquire the short-lived speech sounds of their native language during their first year of life. Here, we trace the early buildup of the infant phoneme inventory with naturalistic electroencephalogram. We apply the recent method of deconvolution modeling to capture the emergence of the feature-based phoneme representation that is known to govern speech processing in the mature brain. Our cross-sectional analysis uncovers a gradual developmental increase in neural responses to native phonemes. Critically, infants appear to acquire those phoneme features first that extend over longer time intervals—thus meeting infants’ slow processing abilities. Shorter-lived phoneme features are added stepwise, with the shortest acquired last. Our study shows that the ontogenetic acceleration of electrophysiology shapes early language acquisition by determining the duration of the acquired units.

PHONEMIC BASIS OF THE LETTERS OF THE ALPHABET BY A. BAITURSYNULY

The study of the phonemic basis of the letters of the alphabet is an urgent problem. This is because it allows us to better understand the structure of language and its sound composition. The study of the phonemic basis of the letters of the alphabet is necessary for the correct pronunciation of words, as well as for the development of reading and writing skills. The phonemic basis of the letters of the alphabet in Kazakh linguistics originates in the works of A. Baitursynuly. One of the good deeds of Akhmet Baitursynuly for the people is the creation of the Kazakh alphabet in accordance with the sound approach of the Kazakh language. It is known that the first Kazakh phonologist, who specially studied the system of sounds of the Kazakh language and compiled the alphabet, spent his entire adult life cleaning the Kazakh written language from the lexical, syntactic influence of other languages. The phonemic basis of the letters of the alphabet is determined by the sound composition of the language. A. Baitursynuly is a scientist who first of all accurately determined the sound composition of the Kazakh language to create the alphabet. Therefore, the scientist's alphabet corresponded to the nature and laws of language. In this regard, the paper examines the phonetic and phonemic basis of the letters in the alphabet of A. Baitursynuly and it turns out the letters in the alphabet compiled were created according to a certain grammatical principle. It is established that the letters in the alphabet were chosen according to the phonemic principle and there are linguistic principles for obtaining signs, depending on the general, distinctive features of phonemes. In addition, the study reveals that the alphabet of A. Baitursynuly is formed depending on the systems of sound formation, pronunciation, and audibility, distinguishing not only a sign but also a sign with phonological content.

Distinct roles of delta- and theta-band neural tracking for sharpening and predictive coding of multi-level speech features during spoken language processing.

The brain tracks and encodes multi-level speech features during spoken language processing. It is evident that this speech tracking is dominant at low frequencies (<8 Hz) including delta and theta bands. Recent research has demonstrated distinctions between delta- and theta-band tracking but has not elucidated how they differentially encode speech across linguistic levels. Here, we hypothesised that delta-band tracking encodes prediction errors (enhanced processing of unexpected features) while theta-band tracking encodes neural sharpening (enhanced processing of expected features) when people perceive speech with different linguistic contents. EEG responses were recorded when normal-hearing participants attended to continuous auditory stimuli that contained different phonological/morphological and semantic contents: (1) real-words, (2) pseudo-words and (3) time-reversed speech. We employed multivariate temporal response functions to measure EEG reconstruction accuracies in response to acoustic (spectrogram), phonetic and phonemic features with the partialling procedure that singles out unique contributions of individual features. We found higher delta-band accuracies for pseudo-words than real-words and time-reversed speech, especially during encoding of phonetic features. Notably, individual time-lag analyses showed that significantly higher accuracies for pseudo-words than real-words started at early processing stages for phonetic encoding (<100 ms post-feature) and later stages for acoustic and phonemic encoding (>200 and 400 ms post-feature, respectively). Theta-band accuracies, on the other hand, were higher when stimuli had richer linguistic content (real-words > pseudo-words > time-reversed speech). Such effects also started at early stages (<100 ms post-feature) during encoding of all individual features or when all features were combined. We argue these results indicate that delta-band tracking may play a role in predictive coding leading to greater tracking of pseudo-words due to the presence of unexpected/unpredicted semantic information, while theta-band tracking encodes sharpened signals caused by more expected phonological/morphological and semantic contents. Early presence of these effects reflects rapid computations of sharpening and prediction errors. Moreover, by measuring changes in EEG alpha power, we did not find evidence that the observed effects can be solitarily explained by attentional demands or listening efforts. Finally, we used directed information analyses to illustrate feedforward and feedback information transfers between prediction errors and sharpening across linguistic levels, showcasing how our results fit with the hierarchical Predictive Coding framework. Together, we suggest the distinct roles of delta and theta neural tracking for sharpening and predictive coding of multi-level speech features during spoken language processing.

Is retroflexion a stable cue for distributional learning for speech sounds across languages? Learning for some bilingual adults, but not generalisable to a wider population in a well powered pre-registered study.

Bilinguals are widely reported to have certain kinds of cognitive advantages, including language learning advantages. One possible pathway is a language-specific transfer effect, whereby sensitivity to structural regularities in known languages can be brought to to-be-acquired languages that share particular features. Here we tested for transfer of a specific linguistic property, sensitivity to retroflexion as contrastive phonemic feature. We designed a task for bilinguals with homogeneous language exposure (i.e., bilingual in the same languages) and heterogeneous feature representation (i.e., differing levels of proficiency). Hindi and Mandarin Chinese both have retroflexion in phoneme contrasts (Hindi: stop consonants, Mandarin: sibilants). In a preregistered study, we conducted a statistical learning task for the Hindi dental-retroflex stop contrast with a group of early parallel English-Mandarin bilinguals, who varied in their Mandarin understanding levels. We based the target sample size on power analysis of a pilot study with a Bayesian stop-rule after minimum threshold. Contrary to the pilot study (N = 15), the main study (N = 50) did not find evidence for a learning effect, nor language-experience variance within the group. This finding suggests that statistical effects for the feature in question may be more fragile than commonly assumed, and may be evident in only a small subsample of the general population (as in our pilot). These stimuli have previously shown learning effects in children, so an additional possibility is that neural commitment to adults' languages prevents learning of the fine-grained stimulus contrast in question for this adult population.

A tradeoff between acoustic and linguistic feature encoding in spoken language comprehension.

When we comprehend language from speech, the phase of the neural response aligns with particular features of the speech input, resulting in a phenomenon referred to as neural tracking. In recent years, a large body of work has demonstrated the tracking of the acoustic envelope and abstract linguistic units at the phoneme and word levels, and beyond. However, the degree to which speech tracking is driven by acoustic edges of the signal, or by internally-generated linguistic units, or by the interplay of both, remains contentious. In this study, we used naturalistic story-listening to investigate (1) whether phoneme-level features are tracked over and above acoustic edges, (2) whether word entropy, which can reflect sentence- and discourse-level constraints, impacted the encoding of acoustic and phoneme-level features, and (3) whether the tracking of acoustic edges was enhanced or suppressed during comprehension of a first language (Dutch) compared to a statistically familiar but uncomprehended language (French). We first show that encoding models with phoneme-level linguistic features, in addition to acoustic features, uncovered an increased neural tracking response; this signal was further amplified in a comprehended language, putatively reflecting the transformation of acoustic features into internally generated phoneme-level representations. Phonemes were tracked more strongly in a comprehended language, suggesting that language comprehension functions as a neural filter over acoustic edges of the speech signal as it transforms sensory signals into abstract linguistic units. We then show that word entropy enhances neural tracking of both acoustic and phonemic features when sentence- and discourse-context are less constraining. When language was not comprehended, acoustic features, but not phonemic ones, were more strongly modulated, but in contrast, when a native language is comprehended, phoneme features are more strongly modulated. Taken together, our findings highlight the flexible modulation of acoustic, and phonemic features by sentence and discourse-level constraint in language comprehension, and document the neural transformation from speech perception to language comprehension, consistent with an account of language processing as a neural filter from sensory to abstract representations.

Экспрессивная функция сегментных единиц поэтического текста: универсальные и национально-специфические признаки

&lt;p&gt;The article focuses on the system of expressive means of poetic text. There are two ways how to research the poetic text: the first is based on the linguistic approach while the second is based on the perception of the emotional mood of the poetic text. The aim of the research is to identify the correlation between the formal features of the poetic text in the phonological aspect and the way its emotional mood is conveyed and perceived in the text. The object of the research is sound poetic texts. The subject is the elements of the segmental level in their expressive function. The novelty of the approach is as follows: there is an attempt to combine the structural (phonological) analysis with the psycholinguistic data in the field of the theory of emotionality, which allows us to offer the mechanism of research of the sound-pictorial possibilities of a poetic text, based on the frequency of differential signs of phonemes. The authors rely on E.N. Vinarskaya&amp;rsquo;s basic emotions concept. A psycholinguistic experiment on the perception of sounding German poetic texts by native speakers of German and Russian is carried out. The linguistic part of the experiment includes phonetic notation of poetic texts by phonetic auditors. After that the received data of the psycholinguistic and linguistic parts of the experiment is analyzed for the correlation of the associated emotion and the frequency of realization of phonological features of vowel phonemes by localization (row and timbral characteristics of vowels) and fullness (short-open-unstressed and long-closed-unstressed). The analysis of the data is comparative in relation to two groups of informants in German and Russian. The comparative aspect of the study allows us to identify universal and nationally specific signs of perception and correlate it with the basic emotions. Universal feature is the fact that the complexes of phonologically relevant features function as signs in shaping the emotional image of a word. Nationally specific feature reflects the frequency of realization in accordance with the associated emotion in the perception of German and Russian speakers.&lt;/p&gt;

The Euclidean metrics applied to the interphonemic distance measurements

The similarity or dissimilarity of the spoken words is generally rendered by intuition, depending on the personal orientation or the personal traits of a listener/speaker. The existing methods of phonetic encoding of words suffer from a number of shortcomings, the main one being the impossibility of weighing spoken words in quantitative terms. Moreover, the existing methods may be related to a certain language or language family. The algorithm advanced in the present paper compares the characteristics of different phonemes that make up a word. The paper treats phonemic frequency and sonority as elements common both for consonants and vowels, backness and openness, as features pertaining to vowels, and the place of articulation pertaining to consonants only. The algorithm in question permits to compare in quantitative terms the words of different length, whether formed by open or closed syllables. The inter-phonemic distances are calculated by employing Euclidean metrics. The paper suggests fields of application of the method treated in the paper: this scheme can be applied in the fields of comparative linguistics, in medicine, when the hearing disorders are scrutinized, as well as in the brain cortex mapping.

Tactile dimensions of fabrics expressed by Japanese onomatopoeic words and phonemic features related to fabric luxuriousness and pleasantness

This study examined the perceptual dimensions in the tactile perception of fabrics (cloths). The participants were asked to touch, stretch, and curl 23 fabrics and report suitable onomatopoeic (mimetic) words to describe the hand feel for each fabric. The participants' responses were collated into a contingency table of fabrics according to onomatopoeic words. The participants also rated the hand feel of the fabrics using bipolar semantic differential (SD) scales (heavy-light, thick-thin, soft-hard, elastic-inelastic, warm-cold, rough-smooth, luxurious-cheap, and pleasant-unpleasant). Three dimensions of thickness/heaviness, roughness, and softness were derived by factor analysis of the SD ratings with varimax rotation. Next, the dimensions of warmness/fullness/softness, roughness-smoothness, and elasticity/softness were obtained through correspondence analysis combined with sparse coding applied to the contingency table of fabrics by onomatopoeic words. However, these dimensions were not consistent with the factor dimensions obtained using factor analysis, which suggests that Japanese onomatopoeic words express the complex hand feel of fabrics. Finally, the luxuriousness and pleasantness of fabrics were also examined, and sensory characteristics and phonemes of onomatopoeic words related to these attributes were reported.

Distinctive Features of Phonemes and First Language Acquisition by a Normally Developing Child Without Hearing Impairment: Evidence from Slovak

Speech audiometry uses various types of perceptual tests for the diagnostics of hearing impairment. One of them is a matrix test (Akeroyd et al., 2015), originally prepared for adults, which fully respects the phonological rules of a language, including neutralizations of phonological oppositions based on distinctive features. The shorter version of this test, a so-called reduced matrix test, can be helpful in speech audiometry focusing on children and infants. The goal of our paper is to find out if distinctive features of phonemes are important in the process of first language acquisition. If yes, reduced matrix test aimed at children and infants can be created as a mere simplification of the matrix designed for adults (Panocová &amp; Gregová, 2019). However, if distinctive features do not affect first language acquisition, completely new matrix has to be prepared for infants. The research was based on the longitudinal observation of two infants whose mother tongue is Slovak.

Tracing Vowel Quality in Iraqi Arabic Dialects: A Typological Study

Iraqi Arabic dialects have a range of phoneme inventories, and there is a necessity for tracing the distribution of phonological features of such phonemes within these dialects. Three dialects were investigated to trace variations in vowel quality tracking any distribution patterns or deviations. The first three formants were extracted and analyzed with the application of a number of statistical methods. It is shown that a correlation is evident among the formants of qeltu and gelet dialects with a strong correlation between short and long vowels within and across dialects for two age categories. While the results of ANOVA and MANOVA techniques show a significant interaction of F1 and F2 across dialects. It is crucial to say that a pattern of variation in vowel quality is diagnosed among Iraqi Arabic dialects.

The Syllable And Word In Diadochokinetic Javanese - Indonesia Speakers

Background: Diadochokinetic is an activity used to measure individual’s ability of spelling a speech sound, syllable, and word. Age and language is two variables affecting diadochokinetic activity. Although researches have been conducted on some languages to find out diadochokinetic profile, diadochokinetic data in Indonesian is required. This study was conducted to find out the correlation between syllable and word, and phonemic characteristics. &#x0D; Methods: Data was collected by involving skilled enumerator. Data was taken in January 2020. The respondents of research were 100 students in Surakarta City. Diadochokinetic instrument consisted of syllable and world levels composed of one, two, and three syllables. Statistic analysis was carried out using Spearman correlational test and logistic regression. &#x0D; Results: Statistic values for the word with two syllables are mean 3.51; SD: 0.79; CI 95%: 3.35-3.67. Those for the word with one syllable are mean 5.08; SD: 1.31; CI 95%: 4.82-5.34. The repetition time for the word with one syllable is shorter than that with two syllables. &#x0D; Conclusion: The result of statistics shows that there is a mean value of word group. Research area is important to be expanded to measure the broader contextual variation. Researches on diadochokinetic in other age groups need to be done to compare the data yielded.