Thesis (Ph. D.)--University of Wisconsin--Madison, 1958. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 102-104).

Thesis (B.Sc)--University of Hong Kong, 2002. / "A dissertation submitted in partial fulfilment of the requirements for the Bachelor of Science (Speech and Hearing Sciences), The University of Hong Kong, May 10, 2002." Also available in print.

Thesis (B.Sc)--University of Hong Kong, 1993. / "A dissertation submitted in partial fulfilment of the requirements for the Bachelor of Science (Speech and Hearing Sciences), The University of Hong Kong, April 30, 1993." Also available in print.

Thesis (B.Sc)--University of Hong Kong, 1996. / "A dissertation submitted in partial fulfilment of the requirements for the Bachelor of Science (Speech and Hearing Sciences), The University of Hong Kong, April 29, 1996." Also available in print.

Thesis (B.Sc)--University of Hong Kong, 1999. / "A dissertation submitted in partial fulfilment of the requirements for the Bachelor of Science (Speech and Hearing Sciences), The University of Hong Kong, 14th May, 1999." Also available in print.

Thesis (M.S.)--Florida State University, 2006. / Advisor: Julie Stierwalt, Florida State University, College of Communication, Dept. of Communication Disorders. Title and description from dissertation home page (viewed Sept. 22, 2006). Document formatted into pages; contains vi, 26 pages. Includes bibliographical references.

Thesis (Ph.D.)—Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981. / Includes bibliographical references (leaves 185-192). / This electronic version was scanned from a copy of the thesis on file at the Speech Communication Group. The certified thesis is available in the Institute Archives and Special Collections. / Whitaker Health Sciences Fund.

Thesis (M.S.L.P.)--Duquesne University, 2005. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p. 73-76) and index.

In the spirit of interdisciplinary critical accounting studies and in light of the IASB’s on-going Conceptual Framework project, this thesis problematises and proposes a re-conceptualisation of two fundamental financial accounting practices: recognition and measurement of assets. In order to do so, the thesis steps outside financial accounting’s conventional disciplinary resources of economics and finance. It proposes to mobilise Ferdinand de Saussure’s semiology, which, defined as a theory of social sign systems, provides a meaningful delineation of financial accounting as a purposeful sign technology. With such a lens and with a research approach of going beyond IASB’s proclaimed concepts and narrative to its nuanced prescriptions, the thesis challenges taken-for-granted assumptions with regard to the market-based nature of Fair Value measurement and the characterisation of judgement involved in recognition. With respect to value measurement, the thesis harness semiology to fracture the dichotomy between the market and the entity perspectives, which is generally assumed in extant accounting research and policy-making. It is shown how the IASB’s Fair Value measurement prescriptions demonstrate semiology's two-dimensional 'value constellation', where the asset’s value is not merely relational (and not intrinsic) but, importantly, relational in two distinct dimensions. It is a product, first, of differentiation from other values in the market and, second, of interrelation with other values in the specific entity. With a semiological theorisation of the financial statement, market-based and entity-specific perspectives serve as complementary inputs rather than contradictory outputs. With respect to recognition, the thesis proposes to shift the locus of judgment from questions of recognition thresholds (probability and reliability) to the under-investigated issue of the asset’s separability from the firm’s general cash flow. It is shown how the IASB’s procedures manifest the semiological principle of ‘reciprocal articulation’: accounting entities (e.g., ‘assets’) are not passive representations of pre-existing economic resources, but rather a product of delimiting – carving out – the asset/resource from the broader category (or the entire firm). With such theorisation, the crux of recognition is separability, which is never natural or technical, but rather anchorless and reciprocal. The thesis thereby sheds light on the plasticity of recognition for both tangible and intangible assets. With its theory-informed analysis the thesis offers a set of conceptual instruments – value constellation and reciprocal articulation – as the logic of the balance sheet as a sign technology: its semio-logic. With Saussure’s ground-breaking linguistic semiology, it offers a parallel financial-numeric semiology: an Accounting Semiology.

abstract: Audio signals, such as speech and ambient sounds convey rich information pertaining to a user’s activity, mood or intent. Enabling machines to understand this contextual information is necessary to bridge the gap in human-machine interaction. This is challenging due to its subjective nature, hence, requiring sophisticated techniques. This dissertation presents a set of computational methods, that generalize well across different conditions, for speech-based applications involving emotion recognition and keyword detection, and ambient sounds-based applications such as lifelogging. The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance. Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems. Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface. The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015