Speech Recognition

Pronunciation, long on the periphery of applied linguistics research and pedagogy, continues to grow in importance because of its central roles in speech recognition, speech perception, and speaker identity. Pronunciation-related issues such as comprehensibility, accent, and the mutual intelligibility of varieties of world Englishes are central to many questions in applied linguistics. This calls for a sophisticated understanding of how technological tools that have long been used to shed light on phonological categories can be applied to teaching. Research into
computer-assisted pronunciation teaching (CAPT) suggests that both researchers and pronunciation teachers increasingly make use of technology to answer key questions, to ensure that claims are defensible, and to develop theories and practices that more
closely match acoustic reality. This article reviews three key areas where computer technology and pronunciation intersect: (1) appropriate pedagogical goals and the measurement of improvement; (2) the ability of CAPT to give useful, automatic
feedback; and (3) the use of technology in diagnosing pronunciation errors. This article concludes with recommendations for key technological competencies needed
by any researcher or teacher who examines pronunciation-related issues.

Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence.
Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines.
Despite the great efforts of the past decades, however, a natural and
robust human-machine speech interaction still appears to be out of reach,
especially when users interact with a distant microphone in noisy and re-
verberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one
of the major open challenges in the field.
This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called “network of deep neural networks”.
The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.

This chapter provides an overview of research on the phonetic changes that occur in one's native language (L1) due to recent experience in another language (L2), a phenomenon known as phonetic drift. Through a survey of empirical findings on segmental and suprasegmental acoustic properties, the chapter examines the features of the L1 that are subject to phonetic drift, the cognitive mechanism(s) behind phonetic drift, and the various factors that influence the likelihood of phonetic drift. In short, virtually all aspects of L1 speech are subject to drift, but different aspects do not drift in the same manner, possibly due to multiple routes of L2 influence coexisting at different levels of L1 phonological structure. In addition to the timescale of these changes, the chapter discusses the relationship between phonetic drift and attrition as well as some of the enduring questions in this area.

Abstract: The article examines Polish translations of the Derridian term différance. Polish philosophical discourse uses the following renditions of différance: róż (ni (c) oś) ć by Bogdan Banasiak, różNICa by Tadeusz Sławek, gra-na-zwłokę-o-różnicę by Stanisław Cichowicz and the most popular: różnia by Joanna Skoczylas. Should a mistake be deliberately committed in Polish, as it was done in the original? Or should it be corrected, and if so–how to explain the correction?

As with anything written or spoken the language that is used is always from somebody’s viewpoint (Wood-Wallace, 2011: 2) and due to the fact that there are a multitude of different lexical items and syntactical structures for the speaker to choose from, the recipient of communication not only has to understand the literal meaning of the string of words and sentences composed by the speaker or writer but also what the intention of the communicative piece is (Grice, 1989: 86; Huang, 2009: 118). For example ‘It’s hot in here’ could pragmatically mean ‘open the window’, or ‘I’m hungry’ could equally mean ‘I don’t want to go to bed yet’ (Peccei, 1999: 3). Speech is never ‘value free’, it is an encoded piece of communication that in turn is decoded and understood by the recipient. This decoding of sentence meaning, word structure and the lexicon has been studied for a number of years by many linguists (Atlas, 2005: 37, Blunter, 1998: 115; 2004: 488, Grice, 1975: 117; 1989: 86, McCawley, 1978: 245), with a separate branch of lexical pragmatics emerging in the 1990s (Huang, 1998: 1; 2009: 118). The aims of the current essay are to venture further in the semantic-pragmatic field to look more specifically within the realms of entailment and implicature; with the intent of explaining the differences between both entailed and implicated utterances. Equally, the essay will also look into the relationship between semantic and pragmatic utterances and attempt to draw similarities in how entailment and implicature relate to these distinctions. The text will look into how speakers encode and decode language and how context plays a big part in this. Finally, the essay will look at Grice’s Maxims and the Cooperative Principle and attempt to discuss features of conversational implicature with reference to these.

In this paper, we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques. We first discuss models such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) that can effectively exploit variablelength contextual information, and their various combination with other models. We then describe models that are optimized end-to-end and emphasize on feature representations learned jointly with the rest of the system, the connectionist temporal classification (CTC) criterion, and the attention-based sequenceto-sequence translation model. We further illustrate robustness issues in speech recognition systems, and discuss acoustic model adaptation, speech enhancement and separation, and robust training strategies. We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.

A simple yet complex approach to modern sophistication.
In this project we used the MFCC approach to build a unique and accurate coefficients extracting processor to extract feature from the voice stored in the database, then on the next stage we again emoployed the use of MFCC to extract coefficients and used vector quantization to match with the user database. For vector Quantization we used Linde-Buzo Grey Algorithm..
Code is available on demand. Send me a private message if you want it.

It has been argued that depending on gender language is used in different ways in society, and the fact that language is surveyed differently leads to the thesis that men and women have ‘differentiated communicative competence’ (Coates, 2004). A distinct pronunciation variant that is widely used and ‘has recently been noted as spreading through non-standard accents in England’ (cf. Trudgill, 1988: 43), is that of th-fronting. The form th- fronting has become more and more integrated into the speech patterns of many speakers across the United Kingdom although it has ‘not ‘officially’ entered RP yet’ (Altendorf, 1999: 5). This study focused on whether or not gender is associated with variation in the usage of th-fronting in the urban and multicultural locality of Nottingham, United Kingdom. The analysis was undertaken in order to assess how urban youth culture in Nottingham utilizes and applies the form th-fronting in everyday dialogue such as day-to-day conversations, and whether their mode of expression varies according to gender. The study aimed to gain an insight into the relationship between spoken language and identity, and to recognise whether the social backgrounds/ cultural practices people have influence their use of ethnically and age-coded variables. From the results, it was established that th-fronting does vary according to gender and that increased formality does limit the amount of th-fronting used.

Successful speech perception requires that listeners map the acoustic signal to linguistic categories. These mappings are not only probabilistic, but change depending on the situation. For example, one talker's /p/ might be physically indistinguishable from another talker's /b/ (cf. *lack of invariance*). We characterize the computational problem posed by such a subjectively non-stationary world and propose that the speech perception system overcomes this challenge by (1) recognizing previously encountered situations, (2) generalizing to other situations based on previous similar experience, and (3) adapting to novel situations. We formalize this proposal in the {\em ideal adapter} framework: (1) to (3) can be understood as inference under uncertainty about the appropriate generative model for the current talker, thereby facilitating robust speech perception despite the lack of invariance. We focus on two critical aspects of the ideal adapter. First, in situations that clearly deviate from previous experience, listeners need to adapt. We develop a distributional (belief-updating) learning model of incremental adaptation. The model provides a good fit against known and novel phonetic adaptation data, including perceptual recalibration and selective adaptation. Second, robust speech recognition requires listeners learn to represent the *structured* component of cross-situation variability in the speech signal.  We discuss how these two aspects of the ideal adapter provide a unifying explanation for adaptation, talker-specificity, and generalization across talkers and groups of talkers (e.g., accents and dialects). The ideal adapter provides a guiding framework for future investigations into speech perception and adaptation, and more broadly language comprehension.

Keywords: speech perception; generalization; adaptation; statistical learning; hierarchical structure; lack of invariance; non-stationarity

Speech recognition atau pengenalan ucapan merupakan teknologi yang
mampu mengenali pembicaraan atau perkataan tanpa memperdulikan siapa
pembicaranya. Masukan berupa suara mampu diubah menjadi text yang mampu
dibaca. Speech recognition banyak di implementasikan dengan perangkat pintar,
mobil, television, ruangan dan masih banyak yang lainnya. Dengan menggunakan
teknologi seperti ini memudahkan kita untuk melakukan perintah menggunakan
suara semisal pada mobile application. 
Speech recognition banyak di implementasikan dengan perangkat pintar,
mobil, televisi, ruangan dan masih banyak yang lainnya. Dengan menggunakan
teknologi seperti ini memudahkan kita untuk melakukan perintah menggunakan
suara semisal pada mobile application. Saat ini perkembangan smartphone sudah
sangat maju. 
Pada penelitian ini, peneliti membuat automatic speech recognition
pengenalan nama hewan berbasis android. Dari uji coba yang dilakukan, aplikasi
ini membantu orang tua dalam mengajarkan anak tentang hewan-hewan
disekitarnya.

[Co-authored with Rene J. Perez, Chloe A. Kimble, and Jin Wang (Valdosta State)] We use speech recognition algorithms daily with our phones, computers, home assistants, and more. Each of these systems use algorithms to convert the sound waves into useful data for processing which is then interpreted by the machine. Some of these machines use older algorithms while the newer systems use neural networks to interpret this data. These systems then produce an output generated in the form of text to be used. A large amount of training data is needed to make these algorithms and neural networks function effectively.

With the advent of spoken dialogue systems (SDS), communication can no longer be considered a human-to-human transaction. It now involves machines. These mechanisms are not just a medium through which human messages pass, but now occupy the position of the other in social interactions. But the development of robust and efficient conversational agents is not just an engineering challenge. It also depends on research in human conversational behavior. It is the thesis of this paper that communication studies is best situated to respond to this need. The paper argues: 1) that research in communication can supply the information necessary to respond to and resolve many of the open problems in SDS engineering, and 2) that the development of SDS applications can provide the discipline of communication with unique opportunities to test extant theory and verify experimental results. We call this new area of interdisciplinary collaboration “computational interpersonal communication” (CIC).

У монографії подано, обґрунтовано та розвинено комплексний пiдxiд до вживання і творення українських тeрмiнiв, заснований на використанні методів природничих наук і мовознавчих аспектів дослідження. Такий підхід створює передумови для того, щоб розглядати термінологію не лише як розділ лексики, сукупність термінів і вчення про них, а насамперед як науку про утворення, розвиток і функціонування термінів. За допомогою послідовного застосування статистичного та аналітичного методів досліджено теоретичні й практичні термінографічні проблеми, розкрито правописні аспекти відтворення запозичених та іншомовних слів. На основі методу акустичних інваріантів досліджено акустичні характеристики звуків української (а також англійської) мови та знайдено їх інваріантні акустичні параметри. У монографії відображено результати автора, одержані в процесі його наукової та викладацької роботи в Державній академії керівних кадрів освіти, Міжрегіональному інституті вдосконалення вчителів, Київському національному лінгвістичному університеті, Київському національному університеті імені Тараса Шевченка, Техаському університеті в Арлінгтоні (США), Єнському університеті імені Фрідріха Шіллера (Німеччина), Гіссенському університеті імені Юстуса Лібіга (Німеччина), Лейпцізькому університеті (Німеччина), Дрезденському технічному університеті (Німеччина), Університеті Країни басків (Іспанія), Українському мовно-інформаційному фонді Національної академії наук України.

Для науковців, викладачів, аспірантів і студентів філологічних факультетів вищих навчальних закладів, а також для лексикографів, фонетистів, перекладачів і фахівців у галузі прикладної лінгвістики, української термінології і термінографії.

CONTENTS
LIST OF SYMBOLS AND ABBREVIATIONS 8
INTRODUCTION 9
PART 1
THEORETICAL AND METHODOLOGICAL PRINCIPLES OF TERMINOLOGY 18
1.1. Term and terminology: underlying statements 18
1.2. Successful term criteria 30
1.3. General tendencies of development of terminological systems 30
1.4. Main lexical semantic relations in term vocabulary (polysemy, homonymy, paronymy, synonymy) 40
1.5. National and international trends of term fund updating 62
PART 2
SEMANTIC, GRAMMATICAL, AND PHONETIC PROBLEMS OF
UKRAINIAN TERMS CODIFICATION 75
Section 2.1. Semantic and morphological qualities of Ukrainian
term vocabulary 75
2.1.1. Alien borrowings as a linguistic and terminological problem 75
2.1.2. Semantic and stylistic features of use of paronyms and pseudosynonyms in the professional language 83
2.1.3. Scientific approach to  terminological use of verbal nouns 112
2.1.4. Principles of representation of active participial forms in the Ukrainian scientific language 125
2.1.5. Possibilities and prospects of national word coinage
in terminology 127
Section  2.2. Acoustic characteristics of Ukrainian speech sounds as a ground
for scientific approach to render the borrowings 135
2.2.1. Phonetics, acoustic analysis, speech recognition 135
2.2.2. Phonetical and phonological language elements 138
2.2.2.1. Basic concepts of phonetics and phonology 138
2.2.2.2. Nonlinearity and non-invariance of speech elements 147
2.2.3. Speech acoustics 152
2.2.3.1. Acoustic theory of speech production 152
2.2.3.2. Speech sounds formants 157
2.2.3.3. Resonators in a human speech tract 159
2.2.3.4. Invariant acoustic speech analysis 162
2.2.3.5. Acoustic characteristics and invariants of
speech sounds 165
2.2.3.6. Interaction between sounds 179
PART 3
PECULIARITIES OF ORTHOGRAPHIC NORM APPLICATION IN SCIENTIFIC LEXICOGRAPHY AND WAYS TO PERFECT
MODERN SPELLING 189
3.1. Modern spelling strategy problems 189
3.2. Problems of modern spelling of terminological
and common vocabulary 191
3.2.1. Genitive case of masculine nouns of second reversal 181
3.2.2. Use of genitive and accusative cases 195
3.2.3. Spelling of compiled adjectives 196
3.2.4. The features of use of Ukrainian prepositions “в” (“у”) and
“до” for denotation of motion aim and direction 198
3.2.5. Stress problem in three-syllable nouns with the prefix “за ” 202
3.3. Denotation of time moments in Ukrainian 206
3.4. Problem of adaptation of alien terms to Ukrainian spelling 211
3.4.1. Study of phonemes /ґ/, /г/, and /х/ 211
3.4.2. “The nine” (“dev’jatka”) rule in the context of Slavic and non Slavic parallels of the Ukrainian language 214
3.4.3. Doubling simplification problems 220
3.4.4. Some “trifles” of spelling perfection 221
3.4.5. Discussion suggests to spelling 222
3.5. Scholar principles to render borrowed and foreign words:
invariant transcription and transliteration 229
3.5.1. Pronunciation and transcription 229
3.5.2. Writing and transliteration 234
CONCLUDING STATEMENTS 255
LIST OF LITERATURE 260
APPENDIX A: Visual representation of speech sounds
(spectrograms, oscillograms) 397
APPENDIX B: Inculcation of the research results
(main documentation) 337
APPENDIX V: Use of the term Χυμεία in Greek 361

More than 2 million people in the United States are unable to speak or use handwriting as an effective and efficient mean of communication (ASHA,2004). This paper discusses a brief history of augmentative alternative communication (AAC) devices. The ultimate goal of an AAC intervention is to enable individuals to efficiently and effectively engage in a variety of interactions. Communication is a critical element of staying connected to society and is the tool for redefining one’s self. This paper discusses the history of AAC, as well as etiologies that may benefit from AAC use. Without access to speech, individuals are severely restricted within communication and participation in all aspects of life. Furthermore, new mobile technology such as the iPad has brought AAC into the mainstream. The iPad and other mobile technologies provide powerful new tools to potentially enhance communication for individuals with developmental disabilities, acquired neurogenic disorders, and degenerative neurological conditions. A brief comparison between today's mobile technologies and traditional AAC devices such as the Picture Exchange Communication System (PECS) and the Voice Output Communication Device (VOCD) proves which speech generating device (SGD) is more beneficial to individuals that require AAC.


Keywords: Augmentative alternative communication; Picture Exchange Communication System; Voice Output Communication; Speech Generating Device; iPad; Mobile technology

Although the Ancients placed great emphasis on delivery, modern rhetorical scholars often overlook the oral dimensions of speech. Speech is powerful because of its ability to elicit a somatic response. Scholars in other disciplines are examining how speech affects the body, but contemporary rhetorical scholarship often overlooks their findings. This essay reviews scholarship both inside and outside of communication studies that demonstrates the interplay between rhetoric and orality, paying particular attention to how scientific scholarship can inform our conception of ethos and pathos. By drawing together scientific and rhetorical scholarship, scholars in all disciplines can gain a greater understanding of how speech affects the mind, body, and soul.

Kinect sensor is being used for the motion control of a mobile robot according to the movements of hands (gesture), as a ubiquitous human machine interface. The robot is in the form of a loader vehicle, which can be used for transportation of goods. After decision making based on gesture recognition, the corresponding command is sent wirelessly to the robot and action is performed according to the movement of the hands. Moreover, Graphical User Interference (GUI) for monitoring and controlling the robot using computer is also implemented in this project. Obstacles avoidance is being used to avoid hurdles that come across the robot’s path using sonar sensors. The robot can also be controlled using speech recognition.

Nowadays, the improvement of speech recognition technology is growing fast and many techniques are presented. However, even the best algorithm with carefully designed system cannot accomplish good performance speech recognition if the system is trained from poor corpus. Therefore, the speech corpus is the basic research that is necessary and should be consistent, balanced, as well as covering all

The principal target of talk affirmation zone is to make techniques and structures for talk commitment to machine. Talk is the basic techniques for correspondence between individuals. For reasons going from inventive enthusiasm about the segments for mechanical affirmation of human talk abilities to longing to robotize fundamental errands which require human machine associations and research in modified talk affirmation by machines has pulled in a ton of thought for quite a while. In light of genuine advances in authentic exhibiting of talk, customized talk affirmation structures today find expansive application in assignments that require human machine interface, for instance, modified call taking care of in telephone frameworks, and request based information systems that give invigorated travel information, stock esteem references, atmosphere reports, Data section, voice correspondence, access to information: travel, keeping cash, Commands, Avoinics, Automobile passage, talk elucidation, Handicapped people (amaze people) general store, railroad reservations.

The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.

Above and beyond even Nonverbal communication (including
body language), is a form of communication that is Fundamental
to ALL forms of communications between ALL Beings not just on
this planet, but in the entire Universe. And that is-Communication
through energy Frequencies and Vibrations. Recent experiments
done by Russian scientists have confirmed this. The facts revealed
by this experiment and specifically its implications in healing the
limiting symptoms of autism using the human voice and Energy in
the form of sound frequencies and vibrations (both audible and
inaudible to the human ear) as an epigenetic tool, is explained
further down in this article.
Communication through Energy Frequencies is not rocket science
and can be Re-Learned by one and all. This can be done with the
help of cutting-edge Epigenetic Tools such as Intent Healing™ and
Applied Energy Medicine, which are available today. Read More ...