本研究旨在探索青春期好友關係中「不理」的互動事件。過去研究將「不理」視為關係攻擊，然而，不理或斷絕關係會發生在好友關係上，且行為者自身對此亦有相當的痛苦感，關係攻擊未能解釋如此弔詭現象。事實上，為了維繫關係的和諧，雙方關係良好者 通常不易以抗爭因應，衝突成為內隱性，不直接撕破臉而以「不理」為傳達不滿的方式。因此，本研究將以黃囇莉（2006）「人際和諧與衝突動態模式」為研究架構，於關係脈絡下重新理解「不理」。並以半結構式的訪談大綱，針對十三名參與者進行深度訪談，蒐集參與者述說其「不理」的經驗歷程的質性資料。 研究結果指出，「不理」的互動歷程展現出青春期友誼拿捏人我距離的練習，並對於後續親密關係的人際互動有所影響。青春期的好友關係為非穩定的自發性情感支持關係，卻由於好友的角色義務不明確，反而令「關係」的親近拿捏成為引發衝突的原因，從親與近的互動浮現關係裡的失合與失調，包括「無心傷害」、「情感性地付出與回報不對等」、「挫敗遷怒」及「拉開距離」，成為主動方心底的內隱衝突。這些主動方主觀知覺的內隱衝突較為隱晦，無正當性據理力爭，加上社會文化對於關係和諧的要求，令主動方身處於「直接吵是傷人，放低姿態溝通卻有損自我」的兩難情境。在顧全大局之下，主動方運用「不理」讓被動方能意識到他的不滿，而使得主動方的內隱衝突有機會浮上雙方互動的檯面，因此看似無互動的不理，實則為主動方促始關係改變的方式。 在「不理」的停滯期間，若持續沒有明確的焦點，冷靜之後，衝突淡化而進入虛性和諧。若衝突激化，則會造成關係的斷裂。特別的是，「不理」同時提供將虛性和轉化成實性和諧的機會。讓內隱衝突浮現檯面，若能予以冷靜化，雙方相互溝通，使之成為實性衝突，而能有所聚焦。且在此過程中雙方能表達對關係的重視，反而能讓雙方的關係轉化成更穩定的實性和諧。此外，由於青春期友誼有相當高的情感依附性，即使實際互動早已形同陌路，心裡卻不會輕易認定關係結止，既然關係未完成，就有復合的一日。由此可知，「不理」其後的結果不一定全是負面，這是以關係攻擊觀點無法看見之處。 本研究將焦點置於關係之中，探索青春期友誼關係中「不理」的因素，提供更細密地資料以理解當事人的難處與心理歷程，並凸顯出友誼關係離合的轉折，讓關係的鬆動與改變有計可施。因而可作為青少年友誼人際衝突化解與結束之預防教育與輔導的參考。 / Previous studies treat ignoring like one type of relationship aggression. However ignoring and ending the relationship happen in close friendships. From the relationship aggression point of view , there is no explanation why the actor feels pain and guilt in this situation. In interpersonal conflict the people who place importance on the relationship can not easily confront it but rather let the conflict become implicit. Not to damage the relationship directly but to ignore the target is one way of coping with conflict. Ignoring passes on an unsatisfied feeling silently. This study used the dynamic model of interpersonal harmony and conflict to explore ignoring. 13 participants were interviewed to report their own ignoring experiences. The results showed that the ignoring process is a practice in how to balance the I-Thou psychological distance and it affects future intimate relationships. Close friendships in adolescence are high support relationships but can be unstable. Due to obscure role obligation, causes of conflict in these relationships include “unwittingly harm”,” inequitable affections”, “anger transferring”, and ”more independent space”. In addition to all of these hidden conflicts are illegitimate reasons. Also, the cultural drive to maintain harmony results in a dilemma where acting out hurts the other but not acting out hurts oneself. There were four results after this “stuck in the mud” period. If the conflict stayed vague, the relationship entered into superficial harmony, where the closeness of the relationship was decreased. If the conflict grew, the resulting relationship was broken. If there was a chance to communicate clearly and express each other’s value in the relationship, the resulting relationship entered into genuine harmony. The last result was an unfinished situation due to the high affection in adolescent friendship. Even though there was no longer contact, these people did not easily identify the end of the relationship. This suggests, the results after ignoring are not all negative. However from the relationship aggression point of view, positive results cannot be found. Thus, this research suggests using the the interpersonal and harmony views to explore ignoring and it is suggested that understanding the ignoring situation is more effective than blocking it in practice.

<p>The thesis deals with how<strong> </strong>technology as a school subject is presented to the pupils in the Swedish compulsory school at junior high school level. The main focus is on how teachers work with the subject matter in teaching, which is on the level of <em>the</em> <em>enacted curriculum</em>. The official documents established by the national school authorities,<em> the intended curriculum</em>, and <em>the hidden curriculum</em> are both of special interest in the study. The hidden curriculum refers to possible, but not intended consequences of the enacted curriculum for pupils’ understanding of technology as a school subject. </p><p><em>          </em>The empirical analysis of the study is based on a narrative analysis on the one hand and the variation theory on the other. The empirical data collection consists of data from:<strong> </strong>(a) interviews with five teachers and (b) a series of classroom observations, covering an entire section of each teacher’s course of the subject matter.</p><p>          The data from the interviews with these teachers indicated that they understood the concept of technology as<strong> </strong>human made artefacts aiming to satisfy practical needs. When it came to the understanding of technology as a school subject the teachers differed between understanding the aim of the subject as to: (1) practice craftsmanship, (2) prepare the pupils for future careers as engineers, (3) illustrate science, (4) strengthen girls’ technical self-confidence and (5) get the pupils interested in technology in order to become inventors in the future. <strong></strong></p><p>The data from the classroom observations indicated that the teaching presented in technology gave the pupils the opportunity to develop three specific capabilities: (1) evaluate and test functionality, (2) be precise and accurate and (3) construct, build and mount. The three capabilities were possible to develop when accomplishing tasks of practical character. Results also indicated that technology as a school subject was taught in different ways depending on the teachers’ educational background, the physical learning environment and the size of the school class. Variation theory was applied as a tool in the analysis of the data from the classroom observations, i.e. the teachers’ ways of working with the subject matter. The analysis indicated that the most frequently used pattern of variation was ‘contrast’.  Through the contrast-variation the teachers managed to contrast better or worse alternatives of constructing and using artefacts. It can be argued that this pattern of variation, ‘contrast’, is the proper pattern when pupils are working with limited or expensive material.<strong></strong></p><p>          The overall conclusion of the study is that teachers’ interpretations of current intended curriculum and their choices of subject matter and teaching methods affect which abilities the pupils are<strong> </strong>offered to develop in technology as a school subject. Based on the results of the study it can be argued that the education and the teaching of technology lacks realism and the result is that technology as a school subject may be experienced by pupils as not very important. It is obvious that the school subject technology, as well as teaching in technology, in the Swedish compulsory school, demands more attention from the national school authorities, in order to develop the pupils’ understanding that technology as a subject is related to the future development of society and social welfare.<strong></strong></p><p> </p>

Commercial video game developers constantly strive to create intelligent humanoid characters that are controlled by computers. To ensure computer opponents are challenging to human players, these characters are often allowed to cheat. Although they appear skillful at playing video games, cheating characters may not behave in a human-like manner and can contribute to a lack of player enjoyment if caught. This work investigates the problem of predicting opponent positions in the video game Counter-Strike: Source without cheating. Prediction models are machine-learned from records of past matches and are informed only by game information available to a human player. Results show that the best models estimate opponent positions with similar or better accuracy than human experts. Moreover, the mistakes these models make are closer to human predictions than actual opponent locations perturbed by a corresponding amount of Gaussian noise.

Human activity analysis has attracted great interest from computer vision researchers due to its promising applications in many areas such as automated visual surveillance, computer-human interactions, and motion-based identification and diagnosis. This dissertation presents work in two areas: general human activity recognition from video, and human activity analysis for the purpose of identifying pathological gait from both 3D captured data and from video. Even though the research in human activity recognition has been going on for many years, still there are many issues that need more research. This includes the effective representation and modeling of human activities and the segmentation of sequences of continuous activities. In this thesis we present an algorithm that combines shape and motion features to represent human activities. In order to handle the activity recognition from any viewing angle we quantize the viewing direction and build a set of Hidden Markov Models (HMMs), where each model represents the activity from a given view. Finally, a voting based algorithm is used to segment and recognize a sequence of human activities from video. Our method of representing activities has good attributes and is suitable for both low resolution and high resolution video. The voting based algorithm performs the segmentation and recognition simultaneously. Experiments on two sets of video clips of different activities show that our method is effective. Our work on identifying pathological gait is based on the assumption of gait symmetry. Previous work on gait analysis measures the symmetry of gait based on Ground Reaction Force data, stance time, swing time or step length. Since the trajectories of the body parts contain information about the whole body movement, we measure the symmetry of the gait based on the trajectories of the body parts. Two algorithms, which can work with different data sources, are presented. The first algorithm works on 3D motion-captured data and the second works on video data. Both algorithms use support vector machine (SVM) for classification. Each of the two methods has three steps: the first step is data preparation, i.e., obtaining the trajectories of the body parts; the second step is gait representation based on a measure of gait symmetry; and the last step is SVM based classification. For 3D motion-captured data, a set of features based on Discrete Fourier Transform (DFT) is used to represent the gait. We demonstrate the accuracy of the classification by a set of experiments that shows that the method for 3D motion-captured data is highly effective. For video data, a model based tracking algorithm for human body parts is developed for preparing the data. Then, a symmetry measure that works on the sequence of 2D data, i.e. sequence of video frames, is derived to represent the gait. We performed experiments on both 2D projected data and real video data to examine this algorithm. The experimental results on 2D projected data showed that the presented algorithm is promising for identifying pathological gait from video. The experimental results on the real video data are not good as the results on 2D projected data. We believe that better results could be obtained if the accuracy of the tracking algorithm is improved.

Aufgrund seiner nahezu kontinuierlichen eruptiven Aktivität zählt der Merapi zu den gefährlichsten Vulkanen der Welt. Der Merapi befindet sich im Zentralteil der dicht bevölkerten Insel Java (Indonesien). Selbst kleinere Ausbrüche des Merapi stellen deswegen eine große Gefahr für die ansässige Bevölkerung in der Umgebung des Vulkans dar. Die am Merapi beobachtete enge Korrelation zwischen seismischer und vulkanischer Aktivität erlaubt es, mit Hilfe der Überwachung der seismischen Aktivität Veränderungen des Aktivitätszustandes des Merapi zu erkennen. Ein System zur automatischen Detektion und Klassifizierung seismischer Ereignisse liefert einen wichtigen Beitrag für die schnelle Analyse der seismischen Aktivität. Im Falle eines bevorstehenden Ausbruchszyklus bedeutet dies ein wichtiges Hilfsmittel für die vor Ort ansässigen Wissenschaftler.<br /> In der vorliegenden Arbeit wird ein Mustererkennungsverfahren verwendet, um die Detektion und Klassifizierung seismischer Signale vulkanischen Urprunges aus den kontinuierlich aufgezeichneten Daten in Echtzeit zu bewerkstelligen. Der hier verwendete A nsatz der hidden Markov Modelle (HMM) wird motiviert durch die große Ähnlichkeit von seismischen Signalen vulkanischen Ursprunges und Sprachaufzeichnungen und den großen Erfolg, den HMM-basierte Erkennungssysteme in der automatischen Spracherkennung erlangt haben. <br /> Für eine erfolgreiche Implementierung eines Mustererkennungssytems ist es notwendig, eine geeignete Parametrisierung der Rohdaten vorzunehmen. Basierend auf den Erfahrungswerten seismologischer Observatorien wird ein Vorgehen zur Parametrisierung des seismischen Wellenfeldes auf Grundlage von robusten Analyseverfahren vorgeschlagen. Die Wellenfeldparameter werden pro Zeitschritt in einen reell-wertigen Mustervektor zusammengefasst. Die aus diesen Mustervektoren gebildete Zeitreihe ist dann Gegenstand des HMM-basierten Erkennungssystems. Um diskrete hidden Markov Modelle (DHMM) verwenden zu können, werden die Mustervektoren durch eine lineare Transformation und nachgeschaltete Vektor Quantisierung in eine diskrete Symbolsequenz überführt. Als Klassifikator kommt eine Maximum-Likelihood Testfunktion zwischen dieser Sequenz und den, in einem überwachten Lernverfahren trainierten, DHMMs zum Einsatz.<br /> Die am Merapi kontinuierlich aufgezeichneten seismischen Daten im Zeitraum vom 01.07. und 05.07.1998 sind besonders für einen Test dieses Klassifikationssystems geeignet. In dieser Zeit zeigte der Merapi einen rapiden Anstieg der Seismizität kurz bevor dem Auftreten zweier Eruptionen am 10.07. und 19.07.1998. Drei der bekannten, vom Vulkanologischen Dienst in Indonesien beschriebenen, seimischen Signalklassen konnten in diesem Zeitraum beobachtet werden. Es handelt sich hierbei um flache vulkanisch-tektonische Beben (VTB, h < 2.5 km), um sogenannte MP-Ereignisse, die in direktem Zusammenhang mit dem Wachstum des aktiven Lavadoms gebracht werden, und um seismische Ereignisse, die durch Gesteinslawinen erzeugt werden (lokaler Name: Guguran).<br /> Die spezielle Geometrie des digitalen seismischen Netzwerkes am Merapi besteht aus einer Kombination von drei Mini-Arrays an den Flanken des Merapi. Für die Parametrisierung des Wellenfeldes werden deswegen seismische Array-Verfahren eingesetzt. Die individuellen Wellenfeld Parameter wurden hinsichtlich ihrer Relevanz für den Klassifikationsprozess detailliert analysiert. Für jede der drei Signalklassen wurde ein Satz von DHMMs trainiert. Zusätzlich wurden als Ausschlussklassen noch zwei Gruppen von Noise-Modellen unterschieden.<br /> Insgesamt konnte mit diesem Ansatz eine Erkennungsrate von 67 % erreicht werden. Im Mittel erzeugte das automatische Klassifizierungssystem 41 Fehlalarme pro Tag und Klasse. Die Güte der Klassifikationsergebnisse zeigt starke Variationen zwischen den individuellen Signalklassen. Flache vulkanisch-tektonische Beben (VTB) zeigen sehr ausgeprägte Wellenfeldeigenschaften und, zumindest im untersuchten Zeitraum, sehr stabile Zeitmuster der individuellen Wellenfeldparameter. Das DHMM-basierte Klassifizierungssystem erlaubte für diesen Ereignistyp nahezu 89% richtige Entscheidungen und erzeugte im Mittel 2 Fehlalarme pro Tag.<br /> Ereignisse der Klassen MP und Guguran sind mit dem automatischen System schwieriger zu erkennen. 64% aller MP-Ereignisse und 74% aller Guguran-Ereignisse wurden korrekt erkannt. Im Mittel kam es bei MP-Ereignissen zu 87 Fehlalarmen und bei Guguran Ereignissen zu 33 Fehlalarmen pro Tag. Eine Vielzahl der Fehlalarme und nicht detektierten Ereignisse entstehen jedoch durch eine Verwechslung dieser beiden Signalklassen im automatischen Erkennnungsprozess. Dieses Ergebnis konnte aufgrund der ähnlichen Wellenfeldeigenschaften beider Signalklassen erklärt werden, deren Ursache vermutlich in den bekannt starken Einflüssen des Mediums entlang des Wellenausbreitungsweges in vulkanischen Gebieten liegen. <br /> Insgesamt ist die Erkennungsleistung des entwickelten automatischen Klassifizierungssystems als sehr vielversprechend einzustufen. Im Gegensatz zu Standardverfahren, bei denen in der Seismologie üblicherweise nur der Startzeitpunkt eines seismischen Ereignisses detektiert wird, werden in dem untersuchten Verfahren seismische Ereignisse in ihrer Gesamtheit erfasst und zudem im selben Schritt bereits klassifiziert. / Merapi volcano is one of the most active and dangerous volcanoes of the earth. Located in central part of Java island (Indonesia), even a moderate eruption of Merapi poses a high risk to the highly populated area. Due to the close relationship between the volcanic unrest and the occurrence of seismic events at Mt. Merapi, the monitoring of Merapi's seismicity plays an important role for recognizing major changes in the volcanic activity. An automatic seismic event detection and classification system, which is capable to characterize the actual seismic activity in near real-time, is an important tool which allows the scientists in charge to take immediate decisions during a volcanic crisis. <br /> In order to accomplish the task of detecting and classifying volcano-seismic signals automatically in the continuous data streams, a pattern recognition approach has been used. It is based on the method of hidden Markov models (HMM), a technique, which has proven to provide high recognition rates at high confidence levels in classification tasks of similar complexity (e.g. speech recognition). Any pattern recognition system relies on the appropriate representation of the input data in order to allow a reasonable class-decision by means of a mathematical test function. Based on the experiences from seismological observatory practice, a parametrization scheme of the seismic waveform data is derived using robust seismological analysis techniques. The wavefield parameters are summarized into a real-valued feature vector per time step. The time series of this feature vector build the basis for the HMM-based classification system. In order to make use of discrete hidden Markov (DHMM) techniques, the feature vectors are further processed by applying a de-correlating and prewhitening transformation and additional vector quantization. The seismic wavefield is finally represented as a discrete symbol sequence with a finite alphabet. This sequence is subject to a maximum likelihood test against the discrete hidden Markov models, learned from a representative set of training sequences for each seismic event type of interest.<br /> A time period from July, 1st to July, 5th, 1998 of rapidly increasing seismic activity prior to the eruptive cycle between July, 10th and July, 19th, 1998 at Merapi volcano is selected for evaluating the performance of this classification approach. Three distinct types of seismic events according to the established classification scheme of the Volcanological Survey of Indonesia (VSI) have been observed during this time period. Shallow volcano-tectonic events VTB (h < 2.5 km), very shallow dome-growth related seismic events MP (h < 1 km) and seismic signals connected to rockfall activity originating from the active lava dome, termed Guguran.<br /> The special configuration of the digital seismic station network at Merapi volcano, a combination of small-aperture array deployments surrounding Merapi's summit region, allows the use of array methods to parametrize the continuously recorded seismic wavefield. The individual signal parameters are analyzed to determine their relevance for the discrimination of seismic event classes. For each of the three observed event types a set of DHMMs has been trained using a selected set of seismic events with varying signal to noise ratios and signal durations. Additionally, two sets of discrete hidden Markov models have been derived for the seismic noise, incorporating the fact, that the wavefield properties of the ambient vibrations differ considerably during working hours and night time. <br /> A total recognition accuracy of 67% is obtained. The mean false alarm (FA) rate can be given by 41 FA/class/day. However, variations in the recognition capabilities for the individual seismic event classes are significant. Shallow volcano-tectonic signals (VTB) show very distinct wavefield properties and (at least in the selected time period) a stable time pattern of wavefield attributes. The DHMM-based classification performs therefore best for VTB-type events, with almost 89% recognition accuracy and 2 FA/day. <br /> Seismic signals of the MP- and Guguran-classes are more difficult to detect and classify. Around 64% of MP-events and 74% of Guguran signals are recognized correctly. The average false alarm rate for MP-events is 87 FA/day, whereas for Guguran signals 33 FA/day are obtained. However, the majority of missed events and false alarms for both MP and Guguran events are due to confusion errors between these two event classes in the recognition process. <br /> The confusion of MP and Guguran events is interpreted as being a consequence of the selected parametrization approach for the continuous seismic data streams. The observed patterns of the analyzed wavefield attributes for MP and Guguran events show a significant amount of similarity, thus providing not sufficient discriminative information for the numerical classification. The similarity of wavefield parameters obtained for seismic events of MP and Guguran type reflect the commonly observed dominance of path effects on the seismic wave propagation in volcanic environments.<br /> The recognition rates obtained for the five-day period of increasing seismicity show, that the presented DHMM-based automatic classification system is a promising approach for the difficult task of classifying volcano-seismic signals. Compared to standard signal detection algorithms, the most significant advantage of the discussed technique is, that the entire seismogram is detected and classified in a single step.

This work presents new development and applications of ab-initio simulation tools for material science. Focus lies on materials with strong electronic correlation and strong spin-orbit coupling. Improvements on methods for solving the impurity problem in LDA+DMFT is presented, as well as a reliant method for charge self-consistency in a LMTO based electronic structure code. A new adaptive scheme for Brillouin zone integration is developed, where we show a strong reduction of numerical noise compared to standard techniques. A reformulation of the standard LDA+U method aiming to reduce the number of free parameters is introduced. Fast and realistic reduction of the number of free parameters provides the possibility of high throughput calculations and enabled us to study a large number of compounds. An analysis method for polarization in terms of coupled multipoles, and their corresponding energy contributions is developed and applied. This led to the formulation of Katt's rules, a set of rules complementary to Hund's rules. Katt's rules applies for occupying the orbitals of an electronic shell with strong spin-orbit coupling. The analysis is also used to investigate the unconventional Uranium based superconductors URu2Si2, UPt3, UPd2Al3 and UNi2Al3, as well as the high temperature superconductor LaOFeAs. We also investigate the non-magnetic delta-phase of Plutonium, providing insight to the electronic structure and the branching ratios of 4d to 5f transitions seen in photo emission spectra.The influence of surface reconstruction on the magneto crystalline anisotropy is investigated in multilayer Fe/ZnSe, showing that Fe deposited on an unreconstructed interface strongly reduces the uniaxial component of the MAE. We provide a detailed understanding of the magnetic properties of Fe2P, opening possible routes for enhancing the MAE in this system. A general route to strong MAE in nano-laminates is presented, we apply this to propose a candidate with extremely strong anisotropy energy density, 5Fe/2W1-xReX for x=[0.6-0.8].

Membrane proteins comprise around 20-30% of a typical proteome and play crucial roles in a wide variety of biochemical pathways. Apart from their general biological significance, membrane proteins are of particular interest to the pharmaceutical industry, being targets for more than half of all available drugs. This thesis focuses on prediction methods for membrane proteins that ultimately rely on their amino acid sequence only. By identifying soluble protein domains in membrane protein sequences, we were able to constrain and improve prediction of membrane protein topology, i.e. what parts of the sequence span the membrane and what parts are located on the cytoplasmic and extra-cytoplasmic sides. Using predicted topology as input to a profile-profile based alignment protocol, we managed to increase sensitivity to detect distant membrane protein homologs. Finally, experimental measurements of the level of membrane integration of systematically designed transmembrane helices in vitro were used to derive a scale of position-specific contributions to helix insertion efficiency for all 20 naturally occurring amino acids. Notably, position within the helix was found to be an important factor for the contribution to helix insertion efficiency for polar and charged amino acids, reflecting the highly anisotropic environment of the membrane. Using the scale to predict natural transmembrane helices in protein sequences revealed that, whereas helices in single-spanning proteins are typically hydrophobic enough to insert by themselves, a large part of the helices in multi-spanning proteins seem to require stabilizing helix-helix interactions for proper membrane integration. Implementing the scale to predict full transmembrane topologies yielded results comparable to the best statistics-based topology prediction methods.

Characterizing hidden solder joint shapes is essential for electronics reliability. Active thermography is a methodology to identify hidden defects inside an object by means of surface abnormal thermal response after applying a heat flux. This research focused on understanding, modeling, and predicting hidden solder joint shapes. An experimental model based on active thermography was used to understand how the solder joint shapes affect the surface thermal response (grand average cooling rate or GACR) of electronic multi cover PCB assemblies. Next, a numerical model simulated the active thermography technique, investigated technique limitations and extended technique applicability to characterize hidden solder joint shapes. Finally, a prediction model determined the optimum active thermography conditions to achieve an adequate hidden solder joint shape characterization. The experimental model determined that solder joint shape plays a higher role for visible than for hidden solder joints in the GACR; however, a MANOVA analysis proved that hidden solder joint shapes are significantly different when describe by the GACR. An artificial neural networks classifier proved that the distances between experimental solder joint shapes GACR must be larger than 0.12 to achieve 85% of accuracy classifying. The numerical model achieved minimum agreements of 95.27% and 86.64%, with the experimental temperatures and GACRs at the center of the PCB assembly top cover, respectively. The parametric analysis proved that solder joint shape discriminability is directly proportional to heat flux, but inversely proportional to covers number and heating time. In addition, the parametric analysis determined that active thermography is limited to five covers to discriminate among hidden solder joint shapes. A prediction model was developed based on the parametric numerical data to determine the appropriate amount of energy to discriminate among solder joint shapes for up to five covers. The degree of agreement between the prediction model and the experimental model was determined to be within a 90.6% for one and two covers. The prediction model is limited to only three solder joints, but these research principles can be applied to generate more realistic prediction models for large scale electronic assemblies like ball grid array assemblies having as much as 600 solder joints.

This thesis presents work in the area of automatic speech recognition (ASR). The thesis focuses on methods for increasing the efficiency of speech recognition systems and on techniques for efficient representation of different types of knowledge in the decoding process. In this work, several decoding algorithms and recognition systems have been developed, aimed at various recognition tasks. The thesis presents the KTH large vocabulary speech recognition system. The system was developed for online (live) recognition with large vocabularies and complex language models. The system utilizes weighted transducer theory for efficient representation of different knowledge sources, with the purpose of optimizing the recognition process. A search algorithm for efficient processing of hidden Markov models (HMMs) is presented. The algorithm is an alternative to the classical Viterbi algorithm for fast computation of shortest paths in HMMs. It is part of a larger decoding strategy aimed at reducing the overall computational complexity in ASR. In this approach, all HMM computations are completely decoupled from the rest of the decoding process. This enables the use of larger vocabularies and more complex language models without an increase of HMM-related computations. Ace is another speech recognition system developed within this work. It is a platform aimed at facilitating the development of speech recognizers and new decoding methods. A real-time system for low-latency online speech transcription is also presented. The system was developed within a project with the goal of improving the possibilities for hard-of-hearing people to use conventional telephony by providing speech-synchronized multimodal feedback. This work addresses several additional requirements implied by this special recognition task. / QC 20100811

In mobile speech communication, adverse conditions, such as noisy acoustic environments and unreliable network connections, may severely degrade the intelligibility and natural- ness of the received speech quality, and increase the listening effort. This thesis focuses on countermeasures based on statistical signal processing techniques. The main body of the thesis consists of three research articles, targeting two specific problems: speech enhancement for noise reduction and flexible source coder design for unreliable networks. Papers A and B consider speech enhancement for noise reduction. New schemes based on an extension to the auto-regressive (AR) hidden Markov model (HMM) for speech and noise are proposed. Stochastic models for speech and noise gains (excitation variance from an AR model) are integrated into the HMM framework in order to improve the modeling of energy variation. The extended model is referred to as a stochastic-gain hidden Markov model (SG-HMM). The speech gain describes the energy variations of the speech phones, typically due to differences in pronunciation and/or different vocalizations of individual speakers. The noise gain improves the tracking of the time-varying energy of non-stationary noise, e.g., due to movement of the noise source. In Paper A, it is assumed that prior knowledge on the noise environment is available, so that a pre-trained noise model is used. In Paper B, the noise model is adaptive and the model parameters are estimated on-line from the noisy observations using a recursive estimation algorithm. Based on the speech and noise models, a novel Bayesian estimator of the clean speech is developed in Paper A, and an estimator of the noise power spectral density (PSD) in Paper B. It is demonstrated that the proposed schemes achieve more accurate models of speech and noise than traditional techniques, and as part of a speech enhancement system provide improved speech quality, particularly for non-stationary noise sources. In Paper C, a flexible entropy-constrained vector quantization scheme based on Gaus- sian mixture model (GMM), lattice quantization, and arithmetic coding is proposed. The method allows for changing the average rate in real-time, and facilitates adaptation to the currently available bandwidth of the network. A practical solution to the classical issue of indexing and entropy-coding the quantized code vectors is given. The proposed scheme has a computational complexity that is independent of rate, and quadratic with respect to vector dimension. Hence, the scheme can be applied to the quantization of source vectors in a high dimensional space. The theoretical performance of the scheme is analyzed under a high-rate assumption. It is shown that, at high rate, the scheme approaches the theoretically optimal performance, if the mixture components are located far apart. The practical performance of the scheme is confirmed through simulations on both synthetic and speech-derived source vectors. / QC 20100825