Children whose mental age is below ten years lack the ability to utilize incoming information perfectly enough to make fine phonetic distinctions among sounds. This is an experimental study of the growth and interrelationship between sound discrimination ability and mental age.

Dual Energy X-ray Absorptiometry (DXA)-based finite element (FE) modelling has emerged as a potential tool for better assessment of osteoporotic hip fracture risk. Automation of this complex and computationally-intense procedure is the prime requirement for its clinical applicability. The aim of this study was to develop a fully automatic DXA-based finite element tool and assess its discrimination ability and short-term repeatability. The proximal femur was automatically segmented from clinical hip DXA scan and the subject-specific FE model was constructed for simulating sideways fall. Hip fracture risk indices (HFRIs) were calculated using two ways (along a femur cross-section and over a region of interest, ROI). Hip fracture discriminability increased when moved from femur cross-section based to ROI based HFRI calculation. A significant increase in hip fracture discriminability from baseline femoral neck and total hip bone mineral density (BMD) was achieved with ROI based HFRIs. Promising short-term repeatability was observed for HFRIs (coefficient of variation, CV, 3~3.5%). After removing representative poor cases, CVs were less than 3%. These preliminary results establish the potential of the proposed automatic tool for hip fracture risk assessment and justify large-scale clinical evaluation of its ability to predict incident hip fractures. / February 2017

There has been a significant increase in the interest for the task of classifying demographic profiles i.e. race and ethnicity. Ethnicity is a significant human characteristic and applying facial image data for the discrimination of ethnicity is integral to face-related biometric systems. Given the diversity in the application of ethnicity-specific information such as face recognition and iris recognition, and the availability of image datasets for more commonly available human populations, i.e. Caucasian, African-American, Asians, and South-Asian Indians. A gap has been identified for the development of a system which analyses the full-face and its individual feature-components (eyes, nose and mouth), for the Pakistani ethnic group. An efficient system is proposed for the verification of the Pakistani ethnicity, which incorporates a two-tier (computer vs human) approach. Firstly, hand-crafted features were used to ascertain the descriptive nature of a frontal-image and facial profile, for the Pakistani ethnicity. A total of 26 facial landmarks were selected (16 frontal and 10 for the profile) and by incorporating 2 models for redundant information removal, and a linear classifier for the binary task. The experimental results concluded that the facial profile image of a Pakistani face is distinct amongst other ethnicities. However, the methodology consisted of limitations for example, low performance accuracy, the laborious nature of manual data i.e. facial landmark, annotation, and the small facial image dataset. To make the system more accurate and robust, Deep Learning models are employed for ethnicity classification. Various state-of-the-art Deep models are trained on a range of facial image conditions, i.e. full face and partial-face images, plus standalone feature components such as the nose and mouth. Since ethnicity is pertinent to the research, a novel facial image database entitled Pakistani Face Database (PFDB), was created using a criterion-specific selection process, to ensure assurance in each of the assigned class-memberships, i.e. Pakistani and Non-Pakistani. Comparative analysis between 6 Deep Learning models was carried out on augmented image datasets, and the analysis demonstrates that Deep Learning yields better performance accuracy compared to low-level features. The human phase of the ethnicity classification framework tested the discrimination ability of novice Pakistani and Non-Pakistani participants, using a computerised ethnicity task. The results suggest that humans are better at discriminating between Pakistani and Non-Pakistani full face images, relative to individual face-feature components (eyes, nose, mouth), struggling the most with the nose, when making judgements of ethnicity. To understand the effects of display conditions on ethnicity discrimination accuracy, two conditions were tested; (i) Two-Alternative Forced Choice (2-AFC) and (ii) Single image procedure. The results concluded that participants perform significantly better in trials where the target (Pakistani) image is shown alongside a distractor (Non-Pakistani) image. To conclude the proposed framework, directions for future study are suggested to advance the current understanding of image based ethnicity verification. / Acumé Forensic

Die Erforschung des musikalischen Stils hat viele Facetten, darunter die komplexen kognitiven Strategien, die beim Verstehen musikalischer Informationen zum Tragen kommen, die Konstruktion experimenteller Verfahren, mit denen Musik als ästhetisches Phänomen untersucht werden kann, und die Definition des Begriffs ›Stil‹ selbst. In ihrem Beitrag Le regole della musica (1999) analysierten Mario Baroni, Rossana Dalmonte und Carlo Jacoboni einen Korpus von Arien des Barockkomponisten Giovanni Legrenzi und konstruierten mit Hilfe einer generativen Grammatik ein Regelsystem, das in eine Software namens Legre implementiert wurde, die vermeintlich Arien im Stil von Legrenzi ›komponiert‹. Ziel der vorliegenden Studie ist es, die stilistische Validität von Legre mit Hilfe von Methoden aus der kognitiven Psychologie zu überprüfen. Es wurden Experimente mit Musikern und Nicht-Musikern durchgeführt, um festzustellen, ob Legre eine gültige Grammatik von Legrenzi herzustellen in der Lage ist, d.h., ob eine generative Grammatik den Stil eines Komponisten beschreiben und wiedergeben kann. Die Ergebnisse zahlreicher Experimente zeigen einen Unterschied in der Unterscheidungsfähigkeit zwischen Musikern und Nicht-Musikern; die Leistung einer Person im Unterscheidungsprozess hängt nicht nur von der Einarbeitungsphase in die Aufgabe ab, sondern auch vom Vorwissen der Person. Das Zusammenspiel zwischen diesen Daten und theoretischen Überlegungen trägt dazu bei, die Natur des Stils zu erklären. / The investigation of musical style involves many facets, among them the complex cognitive strategies involved in the understanding of musical information, the construction of experimental procedures able to study music as an aesthetic phenomenon, and even the definition of the term “style” at all. In the work Le regole della musica (1999), Mario Baroni, Rossana Dalmonte, and Carlo Jacoboni analysed a corpus of arias by the baroque composer Giovanni Legrenzi, and by means of a generative grammar they constructed a system of rules that was implemented in a software named Legre, which supposedly “composes” arias in Legrenzi’s style. The aim of the present study is to verify the stylistic validity of Legre’s output by using methods adopted in cognitive psychology. Experiments with musicians and non-musicians were designed in order to assess whether Legre is a valid grammar of Legrenzi—that is, whether a generative grammar is able to describe and recreate the style of a composer. The results of numerous experiments reveal a difference in discrimination ability between musicians and non-musicians; a person’s performance in the process of discrimination depends not only on the training phase of the task, but also on one’s prior knowledge. The interaction between these data and theoretical reflection contributes to the explication of the nature of style.

本論文は第二言語話者の発話を高精度で認識するための音素セットの構成方法に関する研究結果を述べている．本論文では，第二言語話者の発話をネイティブ話者の発話とは異なる音響特徴量の頻度分布を持つ情報源とみなし，これを表現する適切な音素セットを構築する手法を提案している．具体的には，対象とする第二言語と母語との調音位置や調音様式などの類似性に加え，同音異義語の発生による単語識別性能の低下を総合した基準に基づき，最適な音素セットを決定する．提案手法を日本人学生の英語発話の音声認識に適用し，種々の条件下で認識精度の向上を検証した． / This dissertation focuses on the problem caused by confused mispronunciation to improve the recognition performance of second language speech. A novel method considering integrated acoustic and linguistic features is proposed to derive a reduced phoneme set for L2 speech recognition. The customized phoneme set is created with a phonetic decision tree (PDT)-based top-down sequential splitting method that utilizes the phonological knowledge between L1 and L2. The dissertation verifies the efficacy of the proposed method for Japanese English and shows that the feasibility of building a speech recognizer with the proposed method is able to alleviate the problem caused by confused mispronunciation by second language speakers. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University