scholarly journals Sound Unit

2021 ◽  
pp. 151-157
Keyword(s):  
Sound Unit

scholarly journals PHONOSEMANTISM AS PART OF THE SIGN SYSTEM OF LANGUAGE

2021 ◽  
Author(s):  
Andriiva S. S.
Keyword(s):  
One Dimension ◽  
Emotional States ◽  
Acoustic Image ◽  
Sign System ◽  
Linguistic Sign ◽  
Different Types ◽  
Powerful Means ◽  
Sound Unit ◽  
Linguistic Units ◽  
To Receive

Phonosemantics is a science with a thousand-year history, the attitude to which is ambiguous. Despite the fact that the main principle of this linguistic discipline about the motivation of the sound unit and the legitimacy of the phenomenon has been repeatedly questioned, although discussions on the universality and specificity of the phenomenon under study continue to this day. Language is the most powerful means of forming thought; social phenomenon that attest to such its main functions as informational, communicative, emotional, cognitive, epistemological, accumulative. All functions are usually implemented not in isolation, but in various combinations, because each statement in most cases is multifunctional. All functions ultimately work for communication, and that's in the sense that the communicative function is leading. Simultaneously with the acquisition of human language, it acquires knowledge about the world around, which significantly shortens and simplifies the path of cognition, protects a person from unnecessary mistakes. F. de Saussure explained the problem of the value of a linguistic sign, arguing that a linguistic sign combines a concept and an acoustic image and has two essential features: arbitrariness (unmotivated) and linearity (unfolding in time and one dimension). The sign is used to indicate an object outside it, to receive, store and transmit information. A sign acquires its meaning only in a certain system, because outside it is not a sign and means nothing. The palette of phonosemantic searches is inexhaustible, as each linguistic and literary-artistic direction in various manifestations considers the symbolism of images of phonemes, phonemes, morphemes, tokens, syntagms, texts. The scope of using linguistic units with existing phonosemantic features is different types of movement, sound, light phenomena, physiological and emotional states of both humans and animals.

scholarly journals PSIKOEDUKASI KESADARAN FONOLOGI DI PENDIDIKAN ANAK USIA DINI KOTA TASIKMALAYA

2018 ◽  
Vol 2 (2a) ◽  
pp. 1-7
Author(s):  
Anggia Suci Pratiwi ◽  
Rikha Surtika Dewi ◽  
Asti Tri Lestari
Keyword(s):  
Phonological Awareness ◽  
Primary Schools ◽  
Detection Methods ◽  
Teacher Observations ◽  
Stage Of Development ◽  
Level Of Difficulty ◽  
Data Collection Technique ◽  
Observation Method ◽  
Final Syllable ◽  
Sound Unit

ABSTRAK   Makalah ini merupakan hasil penelitian yang bertujuan mengimplementasikan psikoedukasi kesadaran fonologi di sekolah dasar. Psikoedukasi kesadaran fonologi merupakan pelatihan yang mengembangkan sensitivitas anak terhadap struktur bunyi. Psikoedukasi ini dilakukan sebagai upaya stimulasi dan optimalisasi terhadap potensi berbahasa yang dimiliki anak sesuai dengan tahap perkembangannya dan memberikan layanan, serta bimbingan yang dibutuhkan anak dalam melewati tahap-tahap periode sensitif yang dilaluinya dengan cara menggunakan berbagai aktivitas praakademik untuk mengembangkan kesadaran fonologi.                 Pendekatan yang digunakan dalam penelitian ini adalah pendekatan kualitatif dengan menggunakan metode deskriptif. Teknik pengumpulan data dalam penelitian ini adalah dengan metode observasi dan wawancara dengan guru. Observasi dilakukan di dalam kelas untuk melihat kesadaran fonologi anak. Upaya guru dalam pengembangan kesadaran fonologi anak didapatkan melalui wawancara dan pengamatan secara langsung. Setelah dilakukan observasi dan wawancara, selanjutnya dilaksanakan psikoedukasi kesadaran fonologi kepada siswa dan guru. Pemilihan metode yang akan digunakan dalam psikoedukasi pada anak dapat disesuaikan dengan tingkat usia anak. Deteksi aliterasi dan deteksi fonem tunggal relatif mudah bagi anak, yaitu untuk mengenali bunyi silabel awal yang sama (pada purwakanti) dan bunyi silabel akhir yang sama (pada sajak) dari kata-kata yang disajikan. Adapun teknik psikoedukasi dapat melalui lagu anak yang bersajak ataupun melalui kegiatan berpantun. Metode dengan tingkat yang lebih sulit yang dapat digunakan seperti metode deteksi fonem tunggal; di sini tingkat kesulitannya sudah meningkat, karena anak harus mengenali unit bunyi yang lebih kecil daripada silebel. Apabila keterampilan tersebut telah dikuasai, lebih lanjut anak dapat diberi pelatihan dengan metode yang semakin tinggi tingkat kesulitannya seperti metode ketukan fonem.   Kata Kunci: Psikoedukasi, Kesadaran Fonologi, Pendidikan Anak Usia Dini.     ABSTRACT   This paper is the result of a study aimed at implementing psychoeducation in phonological awareness in primary schools. Psychoeducation of phonological awareness is a training to develop children's sensitivity to the sound structure. This psychoeducation serves to stimulate and optimize the language potential of children according to the stage of development, to provide services and to provide guidance that children need to go through the sensitive stage in which they use various preschool activities to develop phonological awareness. The approach used in this study is a qualitative approach using descriptive methods. The data collection technique in this study is the observation method and interviews with the teacher. Observations were carried out in the classroom to see the phonological awareness of the child. Teacher's efforts in developing children's phonological awareness achieved through interviews and direct observation. After conducting observations and interviews, then psychoeducation phonological awareness was carried out to students and teachers. The selection of methods to be used in psychoeducation in children can be adjusted to the age level of the child. Alliteration detection and detection of single phonemes are relatively easy for children to recognize, namely the same initial syllable sound (in purwakanti) and the same final syllable sound (in poetry) of the words presented. The psychoeducation technique can consist of children's songs which are poetry, or dance activities. More difficult level methods can be used such as single-phonemic detection methods; here the level of difficulty has increased as the child has to recognize a sound unit that is smaller than the silebel. If these skills have been mastered, furthermore the child can be given training with methods that increase the level of difficulty such as the phoneme knock method.   Kata Kunci: Psychoeducation, Phonological Awareness, Early Childhood Education

Implementation of Frequency Domain Approach Using Instantaneous Mixing Auto Recursive for Separation of Speech Signals

2016 ◽  
Vol 13 (10) ◽  
pp. 6576-6584
Author(s):  
C. Anna Palagan ◽  
K. Parimala Geetha
Keyword(s):  
Frequency Domain ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Speech Signals ◽  
Single Room ◽  
Sound Unit ◽  
Frequency Domain Approach ◽  
Speech Mixtures ◽  
Complete Frequency ◽  
Algorithmic Rule

In the present work a novel algorithmic rule by taking the speech from two different microphones and separate these speeches by prediction of separating speech mixtures that is predicated on separation matrices is planned. In multi-talker applications so as to boost individual speech sources from their mixtures is done by Blind source Separation (BSS) ways. From the previous published works of separation of speech signals, the main disadvantage is that the incidence of distortion present within the signal that affects separated signal with loud musical noise. The idea for speech separation in standard BSS ways is simply one sound source in a single room. The proposed methodology uses as a network that has the parameters of the IMAR model for the separation matrices over the complete frequency vary. An attempt has been made to estimate the best values of the IMAR model parameters, ΦW and ΦG by suggests that of the maximum-likelihood estimation methodology. Based on the values of these parameters, the source spectral part vectors are estimated. The entire set of TIMIT corpus is employed for speech materials in evolution results. The Signal to Interference magnitude Relation (SIR) improves by a median of 6 dB sound unit over a frequency domain BSS approach.

scholarly journals DIFFICULTIES IN BUILDING THE AUDITORY AND PRONUNCIATION SKILLS IN CHINESE STUDENTS WHILE TEACHING RUSSIAN

2021 ◽  
Vol 15 (1) ◽  
pp. 16-25
Author(s):  
N. M. Skobelkina ◽  
◽  
W. Na ◽  
Keyword(s):  
Foreign Language ◽  
Speech Sound ◽  
Chinese Students ◽  
Russian Language ◽  
Sound Perception ◽  
Sound System ◽  
Sound Unit ◽  
System Differentiation ◽  
Auditory Skills ◽  
The Russian Language

The introduction. The paper deals with a problem of Russian speech sound acquisition by Chinese students, examines the bilateral nature of this problem (sound perception and sound pronunciation), and identifies typical difficulties of the Chinese audience. Materials and methods. The paper analyzes the results of an empirical study aimed at identifying the most frequent difficulties encountered by Chinese students learning the Russian Language when mastering auditory and pronunciation skills. The study relies on the methods of experimental research, statistical data processing, and comparative analysis. Results. The findings show the correlation between the two types of mistakes (in perception and pronunciation of Russian sounds) made by Chinese students. This correlation made it possible to obtain the data on the extent to which the processes of sound unit perception and generation are interconnected and interdependent. The experimental study has identified the most typical difficulties of Chinese students and considered their causes. Conclusion. The study has shown that the number of sound perception mistakes significantly exceeds that of sound pronunciation ones. Therefore, focused work is required to develop auditory skills. Both types of mistakes result from the differences in sound systems of the Russian and Chinese languages, which should be fully taken into account when building audial and pronunciation skills of Chinese students. Keywords: the audial and pronunciation skills, sound system, differentiation of sounds, methods of teaching Russian as a foreign language.

scholarly journals Transcoding Sound to Spelling: Single or Multiple Sound Unit Correspondence?

Cortex ◽  
1987 ◽  
Vol 23 (1) ◽  
pp. 11-28 ◽  
Author(s):  
Doreen M. Baxter ◽  
Elizabeth K. Warrington
Keyword(s):  
Sound Unit

Phonological Awareness Skills in Children With Early and Late Cochlear Implantation: Effects of Task and Phonological Unit

2020 ◽  
Vol 63 (9) ◽  
pp. 2930-2939
Author(s):  
Youngmee Lee
Keyword(s):  
Phonological Awareness ◽  
Cochlear Implantation ◽  
Speech Sound ◽  
Spoken Language ◽  
Receptive Vocabulary ◽  
Unit Size ◽  
Vocabulary Skills ◽  
Age Appropriate ◽  
Sound Unit ◽  
Phonological Unit

Purpose Phonological awareness (PA) skills are critical for spoken language acquisition and literacy. PA manifests in various skills that can be identified based on task performance and speech sound unit size. This study compared the PA skills of children with early cochlear implantation (E-CI), children with late cochlear implantation (L-CI), and children with typical hearing (TH) in relation to task and phonological unit. It also attempted to identify the significant predictors of PA skills in each CI and TH group. Method Twenty children with E-CI, 20 children with L-CI, and 20 children with TH participated in this study. PA skills were assessed using elision, blending, and segmenting tasks at both the syllabic and phonemic levels. Results The E-CI and L-CI groups performed significantly less well than the TH group on the elision and blending tasks at the syllabic level. However, the E-CI group performed at a similar level as the TH group in the segmenting tasks at both the syllabic and phonemic levels. The regression analysis identified age at implantation and receptive vocabulary scores as significant predictors of PA skills in children with CIs. Conclusions Although all the children with CIs had age-appropriate receptive vocabulary skills, the PA skills of both the E-CI and L-CI groups tended to lag behind those of the TH group in the elision and blending tasks at the syllabic level. Age at implantation and receptive vocabulary skills affected the development of PA skills in children with CIs.

Acoustic Modeling of Speech Signal using Artificial Neural Network

2016 ◽  
pp. 196-212
Author(s):  
Mousmita Sarma ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Speech Recognition ◽  
Speech Signal ◽  
Acoustic Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Sound Unit ◽  
Asr System

Acoustic modeling of the sound unit is a crucial component of Automatic Speech Recognition (ASR) system. This is the process of establishing statistical representations for the feature vector sequences for a particular sound unit so that a classifier for the entire sound unit used in the ASR system can be designed. Current ASR systems use Hidden Markov Model (HMM) to deal with temporal variability and Gaussian Mixture Model (GMM) for acoustic modeling. Recently machine learning paradigms have been explored for application in speech recognition domain. In this regard, Multi Layer Perception (MLP), Recurrent Neural Network (RNN) etc. are extensively used. Artificial Neural Network (ANN)s are trained by back propagating the error derivatives and therefore have the potential to learn much better models of nonlinear data. Recently, Deep Neural Network (DNN)s with many hidden layer have been up voted by the researchers and have been accepted to be suitable for speech signal modeling. In this chapter various techniques and works on the ANN based acoustic modeling are described.

Acoustic Modeling of Speech Signal using Artificial Neural Network

2015 ◽  
pp. 282-299 ◽  
Author(s):  
Mousmita Sarma ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Speech Recognition ◽  
Speech Signal ◽  
Acoustic Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Sound Unit ◽  
Asr System

Acoustic modeling of the sound unit is a crucial component of Automatic Speech Recognition (ASR) system. This is the process of establishing statistical representations for the feature vector sequences for a particular sound unit so that a classifier for the entire sound unit used in the ASR system can be designed. Current ASR systems use Hidden Markov Model (HMM) to deal with temporal variability and Gaussian Mixture Model (GMM) for acoustic modeling. Recently machine learning paradigms have been explored for application in speech recognition domain. In this regard, Multi Layer Perception (MLP), Recurrent Neural Network (RNN) etc. are extensively used. Artificial Neural Network (ANN)s are trained by back propagating the error derivatives and therefore have the potential to learn much better models of nonlinear data. Recently, Deep Neural Network (DNN)s with many hidden layer have been up voted by the researchers and have been accepted to be suitable for speech signal modeling. In this chapter various techniques and works on the ANN based acoustic modeling are described.

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