Visible speech improves human language understanding: Implications for speech processing systems

1995 ◽  
Vol 9 (4-5) ◽  
pp. 347-358 ◽  
Author(s):  
Laura A. Thompson ◽  
William C. Ogden
Keyword(s):  
Speech Processing ◽  
Human Language ◽  
Visible Speech ◽  
Language Understanding

scholarly journals An Introduction to Natural Language Processing: the Main Problems

Triangle ◽  
2018 ◽  
pp. 65
Author(s):  
Veronica Dahl
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Language Understanding ◽  
Human Language ◽  
Language Understanding ◽  
Written Text

Natural Language Processing aims to give computers the power to automatically process human language sentences, mostly in written text form but also spoken, for various purposes. This sub-discipline of AI (Artificial Intelligence) is also known as Natural Language Understanding.

scholarly journals Universal Dependencies

2021 ◽  
pp. 1-52
Author(s):  
Marie-Catherine de Marneffe ◽  
Christopher D. Manning ◽  
Joakim Nivre ◽  
Daniel Zeman
Keyword(s):  
Natural Language ◽  
Natural Language Understanding ◽  
Morphological Features ◽  
Linguistic Theory ◽  
Human Language ◽  
Language Understanding ◽  
Good Basis ◽  
Grammatical Relations ◽  
Part Of Speech ◽  
Grammatical Theories

Abstract Universal dependencies (UD) is a framework for morphosyntactic annotation of human language, which to date has been used to create treebanks for more than 100 languages. In this article, we outline the linguistic theory of the UD framework, which draws on a long tradition of typologically oriented grammatical theories. Grammatical relations between words are centrally used to explain how predicate–argument structures are encoded morphosyntactically in different languages while morphological features and part-of-speech classes give the properties of words. We argue that this theory is a good basis for cross-linguistically consistent annotation of typologically diverse languages in a way that supports computational natural language understanding as well as broader linguistic studies.

scholarly journals GAIML: A New Language for Verbal and Graphical Interaction in Chatbots

2008 ◽  
Vol 4 (3) ◽  
pp. 195-209 ◽  
Author(s):  
Roberto Pirrone ◽  
Giuseppe Russo ◽  
Vincenzo Cannella ◽  
Daniele Peri
Keyword(s):  
Natural Language ◽  
Speech Processing ◽  
Complex Interaction ◽  
Markup Language ◽  
Data Types ◽  
Language Understanding ◽  
Graphical Interaction ◽  
Major Direction ◽  
Build Systems ◽  
Museum Guides

Natural and intuitive interaction between users and complex systems is a crucial research topic in human-computer interaction. A major direction is the definition and implementation of systems with natural language understanding capabilities. The interaction in natural language is often performed by means of systems called chatbots. A chatbot is a conversational agent with a proper knowledge base able to interact with users. Chatbots appearance can be very sophisticated with 3D avatars and speech processing modules. However the interaction between the system and the user is only performed through textual areas for inputs and replies. An interaction able to add to natural language also graphical widgets could be more effective. On the other side, a graphical interaction involving also the natural language can increase the comfort of the user instead of using only graphical widgets. In many applications multi-modal communication must be preferred when the user and the system have a tight and complex interaction. Typical examples are cultural heritages applications (intelligent museum guides, picture browsing) or systems providing the user with integrated information taken from different and heterogenous sources as in the case of the iGoogle™ interface. We propose to mix the two modalities (verbal and graphical) to build systems with a reconfigurable interface, which is able to change with respect to the particular application context. The result of this proposal is the Graphical Artificial Intelligence Markup Language (GAIML) an extension of AIML allowing merging both interaction modalities. In this context a suitable chatbot system called Graphbot is presented to support this language. With this language is possible to define personalized interface patterns that are the most suitable ones in relation to the data types exchanged between the user and the system according to the context of the dialogue.

Learner-Centered Language Programs

2006 ◽  
pp. 116-132
Author(s):  
Deryle Lonsdale ◽  
C. Ray Graham ◽  
Rebecca Madsen
Keyword(s):  
Language Learning ◽  
Speech Processing ◽  
Question Answering ◽  
Wide Spectrum ◽  
Research Work ◽  
Human Language ◽  
Language Programs ◽  
Language Technology ◽  
Learner Centered ◽  
Comprehensible Input

In this chapter we first discuss three factors believed to be important for success insecond-language learning: comprehensible input, comprehensible output and noticingdiscrepancies. We then discuss our current research work in integrating variouscomponents of human language technology to address these three language acquisitionfactors. Our efforts involve creating a wide spectrum of interesting language-learningapplications, including question answering and pronunciation tutoring. Theseapplications show the potential of combining speech processing with other importantnatural-language tools, such as external knowledge sources and dialog move engines.The applications we have developed not only show that this integration can besuccessful in creating non-trivial applications, but that there is much work that canbe done to build on what we have accomplished thus far.

Human Language Understanding by Robots

2019 ◽  
pp. 112-118
Author(s):  
Masao Yokota
Keyword(s):  
Human Language ◽  
Language Understanding

Reading Speech from Still and Moving Faces: The Neural Substrates of Visible Speech

2003 ◽  
Vol 15 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Gemma A. Calvert ◽  
Ruth Campbell
Keyword(s):  
Auditory Cortex ◽  
Speech Processing ◽  
Premotor Cortex ◽  
Image Sequence ◽  
Cortical Activation ◽  
Time Varying ◽  
Visible Speech ◽  
Ventral Premotor Cortex ◽  
Natural Time ◽  
Cortical Regions

Speech is perceived both by ear and by eye. Unlike heard speech, some seen speech gestures can be captured in stilled image sequences. Previous studies have shown that in hearing people, natural time-varying silent seen speech can access the auditory cortex (left superior temporal regions). Using functional magnetic resonance imaging (fMRI), the present study explored the extent to which this circuitry was activated when seen speech was deprived of its time-varying characteristics. In the scanner, hearing participants were instructed to look for a prespecified visible speech target sequence (“voo” or “ahv”) among other monosyllables. In one condition, the image sequence comprised a series of stilled key frames showing apical gestures (e.g., separate frames for “v” and “oo” [from the target] or “ee” and “m” [i.e., from nontarget syllables]). In the other condition, natural speech movement of the same overall segment duration was seen. In contrast to a baseline condition in which the letter “V” was superimposed on a resting face, stilled speech face images generated activation in posterior cortical regions associated with the perception of biological movement, despite the lack of apparent movement in the speech image sequence. Activation was also detected in traditional speech-processing regions including the left inferior frontal (Broca's) area, left superior temporal sulcus (STS), and left supramarginal gyrus (the dorsal aspect of Wernicke's area). Stilled speech sequences also generated activation in the ventral premotor cortex and anterior inferior parietal sulcus bilaterally. Moving faces generated significantly greater cortical activation than stilled face sequences, and in similar regions. However, a number of differences between stilled and moving speech were also observed. In the visual cortex, stilled faces generated relatively more activation in primary visual regions (V1/V2), while visual movement areas (V5/MT+) were activated to a greater extent by moving faces. Cortical regions activated more by naturally moving speaking faces included the auditory cortex (Brodmann's Areas 41/42; lateral parts of Heschl's gyrus) and the left STS and inferior frontal gyrus. Seen speech with normal time-varying characteristics appears to have preferential access to “purely” auditory processing regions specialized for language, possibly via acquired dynamic audiovisual integration mechanisms in STS. When seen speech lacks natural time-varying characteristics, access to speech-processing systems in the left temporal lobe may be achieved predominantly via action-based speech representations, realized in the ventral premotor cortex.

scholarly journals Trends In Natural Language Processing : Scope And Challenges

2021 ◽  
pp. 393-401
Author(s):  
Shreyashi Chowdhury ◽  
Asoke Nath
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Human Language ◽  
Statistical Machine Learning ◽  
Language Understanding ◽  
Current Trends ◽  
Language Data ◽  
Voice Data

Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyse large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them.NLP combines computational linguistics—rule-based modelling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation. This paper discusses on the various scope and challenges , current trends and future scopes of Natural Language Processing.

scholarly journals Emergent linguistic structure in artificial neural networks trained by self-supervision

2020 ◽  
Vol 117 (48) ◽  
pp. 30046-30054 ◽  
Author(s):  
Christopher D. Manning ◽  
Kevin Clark ◽  
John Hewitt ◽  
Urvashi Khandelwal ◽  
Omer Levy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hierarchical Structures ◽  
Language Models ◽  
Human Language ◽  
Language Understanding ◽  
Tree Structures ◽  
Linguistic Structure ◽  
Language Communication ◽  
Artificial Neural

This paper explores the knowledge of linguistic structure learned by large artificial neural networks, trained via self-supervision, whereby the model simply tries to predict a masked word in a given context. Human language communication is via sequences of words, but language understanding requires constructing rich hierarchical structures that are never observed explicitly. The mechanisms for this have been a prime mystery of human language acquisition, while engineering work has mainly proceeded by supervised learning on treebanks of sentences hand labeled for this latent structure. However, we demonstrate that modern deep contextual language models learn major aspects of this structure, without any explicit supervision. We develop methods for identifying linguistic hierarchical structure emergent in artificial neural networks and demonstrate that components in these models focus on syntactic grammatical relationships and anaphoric coreference. Indeed, we show that a linear transformation of learned embeddings in these models captures parse tree distances to a surprising degree, allowing approximate reconstruction of the sentence tree structures normally assumed by linguists. These results help explain why these models have brought such large improvements across many language-understanding tasks.

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