Decoding Auditory Attention And Neural Language Processing In Adverse Conditions And Different Listener Groups

This thesis investigated subjective, behavioural and neurophysiological (EEG) measures of speech processing in various adverse conditions and with different listener groups. In particular, this thesis focused on different neural processing stages and their relationship with auditory attention, effort, and measures of speech intelligibility. Study 1 set the groundwork by establishing a toolbox of various neural measures to investigate online speech processing, from the frequency following response (FFR) and cortical measures of speech processing, to the N400, a measure of lexico-semantic processing. Results showed that peripheral processing is heavily influenced by stimulus characteristics such as degradation, whereas central processing units are more closely linked to higher-order phenomena such as speech intelligibility. In Study 2, a similar experimental paradigm was used to investigate differences in neural processing between a hearing-impaired and a normal-hearing group. Subjects were presented with short stories in different levels of multi-talker babble noise, and with different settings on their hearing aids. Findings indicate that, particularly at lower noise levels, the hearing-impaired group showed much higher cortical entrainment than the normal- hearing group, despite similar levels of speech recognition. Intersubject correlation, another global neural measure of auditory attention, however, was similarly affected by noise levels in both the hearing-impaired and the normal-hearing group. This finding indicates extra processing in the hearing-impaired group only on the level of the auditory cortex. Study 3, in contrast to Studies 1 and 2 (which both investigated the effects of bottom-up factors on neural processing), examined the links between entrainment and top-down factors, specifically motivation; as well as reasons for the 5 higher entrainment found in hearing-impaired subjects in Study 2. Results indicated that, while behaviourally there was no difference between incentive and non-incentive conditions, neurophysiological measures of attention such as intersubject correlation were affected by the presence of an incentive to perform better. Moreover, using a specific degradation type resulted in subjects' increased cortical entrainment under degraded conditions. These findings support the hypothesis that top-down factors such as motivation influence neurophysiological measures; and that higher entrainment to degraded speech might be triggered specifically by the reduced availability of spectral detail contained in speech.

Speech recognition in ‘adverse conditions’ has been a familiar area of research in computer science, engineering, and hearing sciences for several decades. In contrast, most psycholinguistic theories of speech recognition are built upon evidence gathered from tasks performed by healthy listeners on carefully recorded speech, in a quiet environment, and under conditions of undivided attention. Building upon the momentum initiated by the Psycholinguistic Approaches to Speech Recognition in Adverse Conditions workshop held in Bristol, UK, in 2010, the aim of this volume is to promote a multi-disciplinary, yet unified approach to the perceptual, cognitive, and neuro-physiological mechanisms underpinning the recognition of degraded speech, variable speech, speech experienced under cognitive load, and speech experienced by theoretically relevant populations. This collection opens with a review of the literature and a formal classification of adverse conditions. The research articles then highlight those adverse conditions with the greatest potential for constraining theory, showing that some speech phenomena often believed to be immutable can be affected by noise, surface variations, or attentional set in ways that will force researchers to rethink their theory. This volume is essential for those interested in speech recognition outside laboratory constraints.

Communication is vital for social participation. However, communication often takes place under suboptimal conditions. This makes communication harder and less reliable, leading at worst to social isolation. In order to promote participation, it is necessary to understand the mechanisms underlying communication in different situations. Human communication is often speech based, either oral or written, but may also involve gesture, either accompanying speech or in the form of sign language. For communication to be achieved, a signal generated by one person has to be perceived by another person, attended to, comprehended and responded to. This process may be hindered by adverse conditions including factors that may be internal to the sender (e.g. incomplete or idiosyncratic language production), occur during transmission (e.g. background noise or signal processing) or be internal to the receiver (e.g. poor grasp of the language or sensory impairment). The extent to which these factors interact to generate adverse conditions may differ across the lifespan. Recent work has shown that successful speech communication under adverse conditions is associated with good cognitive capacity including efficient working memory and executive abilities such as updating and inhibition. Further, frontoparietal networks associated with working memory and executive function have been shown to be activated to a greater degree when it is harder to achieve speech comprehension. To date, less work has focused on sign language communication under adverse conditions or the role of gestures accompanying speech communication under adverse conditions. It has been proposed that the role of working memory in communication under such conditions is to keep fragments of an incomplete signal in mind, updating them as appropriate and inhibiting irrelevant information, until an adequate match can be achieved with lexical and semantic representations held in long term memory. Recent models of working memory highlight an episodic buffer whose role is the multimodal integration of information from the senses and long term memory. It is likely that the episodic buffer plays a key role in communication under adverse conditions. The aim of this research topic is to draw together multiple perspectives on communication under adverse conditions including empirical and theoretical approaches. This will facilitate a scientific exchange among individual scientists and groups studying different aspects of communication under adverse conditions and/or the role of cognition in communication. As such, this topic belongs firmly within the field of Cognitive Hearing Science. Exchange of ideas among scientists with different perspectives on these issues will allow researchers to identify and highlight the way in which different internal and external factors interact to make communication in different modalities more or less successful across the lifespan. Such exchange is the forerunner of broader dissemination of results which ultimately, may make it possible to take measures to reduce adverse conditions, thus facilitating communication. Such measures might be implemented in relation to the built environment, the design of hearing aids and public awareness.

We live in a complex and dynamically changing acoustic environment. To this end, the auditory cortex of humans has developed the ability to process a remarkable amount of diverse acoustic information with apparent ease. In fact, a phylogenetic comparison of auditory systems reveals that human auditory association cortex in particular has undergone extensive changes relative to that of other species, although our knowledge of this remains incomplete. In contrast to other senses, human auditory cortex receives input that is highly pre-processed in a number of sub-cortical structures; this suggests that even primary auditory cortex already performs quite complex analyses. At the same time, much of the functional role of the various sub-areas in human auditory cortex is still relatively unknown, and a more sophisticated understanding is only now emerging through the use of contemporary electrophysiological and neuroimaging techniques. The integration of results across the various techniques signify a new era in our knowledge of how human auditory cortex forms basis for auditory experience. This volume on human auditory cortex will have two major parts. In Part A, the principal methodologies currently used to investigate human auditory cortex will be discussed. Each chapter will first outline how the methodology is used in auditory neuroscience, highlighting the challenges of obtaining data from human auditory cortex; second, each methods chapter will provide two or (at most) three brief examples of how it has been used to generate a major result about auditory processing. In Part B, the central questions for auditory processing in human auditory cortex are covered. Each chapter can draw on all the methods introduced in Part A but will focus on a major computational challenge the system has to solve. This volume will constitute an important contemporary reference work on human auditory cortex. Arguably, this will be the first and most focused book on this critical neurological structure. The combination of different methodological and experimental approaches as well as a diverse range of aspects of human auditory perception ensures that this volume will inspire novel insights and spurn future research.

CONTENTSForeword by Mark Ross, Ph.D. Preface. Contributors. Acknowledgments. Part A: A Theoretical Foundation for the Use of Sound-Field FM Amplification. Rationale for the Use of Sound-Field FM Amplification Systems in Classrooms. Speech-Perception Processes in Children. Speech Perception in the Classroom. Speech Perception in Specific Populations. Part B: Practical Applications of Sound-Field Amplification. Acoustic Measurements in Classrooms. Acoustical Modifications Within Schools. Identifying and Managing the Learning Environment. Sound-Field Amplification: A Review of the Literature. Considerations and Strategies for Amplifying the Classroom. Inservice Training for the Classroom Teacher. Listening Strategies for Teachers and Students. Marketing Sound-Field Amplification Systems. Index.

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.

Although speech is the primary behavioral medium by which humans communicate, its auditory basis is poorly understood, having profound implications on efforts to ameliorate the behavioral consequences of hearing impairment and on the development of robust algorithms for computer speech recognition. In this volume, the authors provide an up-to-date synthesis of recent research in the area of speech processing in the auditory system, bringing together a diverse range of scientists to present the subject from an interdisciplinary perspective. Of particular concern is the ability to understand speech in uncertain, potentially adverse acoustic environments, currently the bane of both hearing aid and speech recognition technology. There is increasing evidence that the perceptual stability characteristic of speech understanding is due, at least in part, to elegant transformations of the acoustic signal performed by auditory mechanisms. As a comprehensive review of speech's auditory basis, this book will interest physiologists, anatomists, psychologists, phoneticians, computer scientists, biomedical and electrical engineers, and clinicians.

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.