<p>The number of text messages sent from mobile phones, has increased dramatically over the last few years. Along with that, we are witnessing a lot of new mobile portal services currently being developed. Many of these services rely on an ability to input text efficiently. The traditional phone keypad is ambiguous because each key encodes more than one letter. At present, the most common way to deal with this problem is using a stored dictionary to guess the intended input. </p><p>This thesis presents a new text entry strategy called Qtap. Instead of using a stored dictionary to guess the intended word, this method uses probabilities of letter sequences. New features that come with Qtap are the usage of the viterbi algorithm to decode input sequences and a non-alphabetic keypad. How the strategy and the keypad used by Qtap were developed, is described throughout the thesis. </p><p>Qtap is also compared to a dictionary-based method, t9, on a non-user level. The results show Qtap is performing well in many senses. The conclusion from this is that a further development of Qtap is motivated. </p><p>A discussion of various modifications and additions to the design, that may yield a performance improvement, is also included.</p>

The number of text messages sent from mobile phones, has increased dramatically over the last few years. Along with that, we are witnessing a lot of new mobile portal services currently being developed. Many of these services rely on an ability to input text efficiently. The traditional phone keypad is ambiguous because each key encodes more than one letter. At present, the most common way to deal with this problem is using a stored dictionary to guess the intended input. This thesis presents a new text entry strategy called Qtap. Instead of using a stored dictionary to guess the intended word, this method uses probabilities of letter sequences. New features that come with Qtap are the usage of the viterbi algorithm to decode input sequences and a non-alphabetic keypad. How the strategy and the keypad used by Qtap were developed, is described throughout the thesis. Qtap is also compared to a dictionary-based method, t9, on a non-user level. The results show Qtap is performing well in many senses. The conclusion from this is that a further development of Qtap is motivated. A discussion of various modifications and additions to the design, that may yield a performance improvement, is also included.

Multimodal Emotion Recognition (MER) has gained increasing attention due to its exceptional performance. In this thesis, we evaluate feature-level fusion, decision-level fusion, and two proposed hierarchical fusion methods for MER systems using a dialogue-based dataset. The first hierarchical approach integrates abstract features across different temporal levels by employing RNN-based and transformer-based context modeling techniques to capture nearby and global context respectively. The second hierarchical strategy incorporates shared information between modalities by facilitating modality interactions through attention mechanisms. Results reveal that RNN-based hierarchical fusion surpasses the baseline by 2%, while transformer-based context modeling and modality interaction methods improve accuracy by 0.5% and 0.6%, respectively. These findings underscore the significance of capturing meaningful emotional cues in nearby context and emotional invariants in dialogue MER systems. We also emphasize the crucial role of text modality. Overall, our research highlights the potential of hierarchical fusion approaches for enhancing MER system performance, presenting systematic strategies supported by empirical evidence. / Multimodal Emotion Recognition (MER) har fått ökad uppmärksamhet på grund av dess exceptionella prestanda. I denna avhandling utvärderar vi feature-level fusion, decision-level fusion och två föreslagna hierarkiska fusion-metoder för MER-system med hjälp av en dialogbaserad dataset. Den första hierarkiska metoden integrerar abstrakta funktioner över olika tidsnivåer genom att använda RNN-baserade och transformer-baserade tekniker för kontextmodellering för att fånga närliggande och globala kontexter, respektive. Den andra hierarkiska strategin innefattar delad information mellan modaliteter genom att underlätta modalitetsinteraktioner genom uppmärksamhetsmekanismer. Resultaten visar att RNN-baserad hierarkisk fusion överträffar baslinjen med 2%, medan transformer-baserad kontextmodellering och modellering av modalitetsinteraktion ökar noggrannheten med 0.5% respektive 0.6%. Dessa resultat understryker betydelsen av att fånga meningsfulla känslomässiga ledtrådar i närliggande sammanhang och emotionella invarianter i dialog MER-system. Vi betonar också den avgörande rollen som textmodalitet spelar. Övergripande betonar vår forskning potentialen för hierarkiska fusion-metoder för att förbättra prestandan i MER-system, genom att presentera systematiska strategier som stöds av empirisk evidens.