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Ethics in Speech and Language Therapy is a key text for students, practitioners and managers alike. The demands of practice, legislation, registration and the recognition of competencies all point to the need for speech and language therapists to be explicitly educated about ethics. This book provides an overview of this key topic, grounds ethical practice in the broader context of morals and values; discusses frameworks for ethical decision making; discusses common ethical issues in speech and language therapy practice and service management; and considers factors which complicate ethical decision making.
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This book provides comprehensive coverage of speech and language disorders arising from pathological processes involving the subcortical structures of the brain. It gives an understanding of these disorders in terms of their neuropathological basis, clinical symptomatology and prognosis. A full discussion of contemporary models and theories of subcortical participation in speech and language processing is given, including discussion of the possible roles of structures such as the basal ganglia, subthalamic nucleus, thalamus and cerebellum. The book covers speech and language disorders associated with a variety of subcortical conditions, ranging from major degenerative conditions such as Parkinsons? Disease, Huntington?s chorea and dystonia, through to acquired non-degenerative subcortical lesions arising from, for example, cerebrovascular accidents and sterotactic surgically induced lesions. In addition, a full description of the relevant assessment and treatment procedures currently recommended for use for each of the subcortical communication disorders is given.
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This book discusses large margin and kernel methods for speech and speaker recognition Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book. Key Features: Provides an up-to-date snapshot of the current state of research in this field Covers important aspects of extending the binary support vector machine to speech and speaker recognition applications Discusses large margin and kernel method algorithms for sequence prediction required for acoustic modeling Reviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech tagging Surveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithms Surveys recent work on kernel approaches to learning a similarity matrix from data This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.
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