There are two research topics in this thesis. First, we implement a highly efficient Chinese input method. Second, we apply a divide-and-conquer scheme to the speaker diarization problem. The implemented Chinese input method transforms an input first-symbol sequence into a character string (a sentence). This means that a user only needs to input a first Mandarin phonetic symbol per character, which is very efficient compared to the current methods. The implementation is based on a dynamic programming scheme and language models. To reduce time complexity, the vocabulary for the language model consists of 1-, 2-, and 3-character words only. The speaker diarization system consists of segmentation and clustering modules. The divide-and-conquer scheme is essentially implemented in the clustering module. We evaluate the performance of our system using the speaker diarization score defined in the 2003 Rich Transcription Evaluation Plan. Compared to the baseline, our method significantly reduces the processing time without compromising diarization accuracy.

In this paper, we investigate a highly efficient input method in Chinese. In the traditional Mandarin phonetic input method, users have to input the complete Mandarin phonetic symbol. The proposed new Chinese input method is which transforms the first Mandarin phonetic symbol sequence to character sequence. Users only have to input the first Mandarin phonetic symbol. Users input first Mandarin phonetic symbol and follow the input rule that spaces are inserted between the words. The system outputs the candidate character sequence hypotheses. Bigram model is used to describe the relation between words. We use the dynamic programing for decoding. We estimate the feasibility for our new Chinese input method and estimate the Stanford segmenter. In the experiment, we estimate the Standford Segmenter works on the simplified Chinese and Traditional Chinese firstly. We observe that the precision and recall on the simplified Chinese are 84.52% and 85.20% which is better than works on the Traditional Chinese 68.43% and 63.43%. And we estimate system efficiency based on language model that trained by WIKI corpus and ASBC corpus separately. The sentence and word accuracy for the ASBC corpus are 39.8% and 70.3%. And the word and character accuracy for WIKI corpus are 20.3% and 53.3%. Finally we estimate the number of candidate hypotheses. The research shows the 10 hypotheses and 20 hypotheses the sentence accuracy are closed.

Blind people cannot enter text on touch devices using common input methods. They use special input methods that have lower performance (i.e. lower entry rate and higher error rate). Most blind people have muscle memory from using classic physical keyboards, but the potential of using this memory is not utilized by existing input methods. The goal of the project is to take advantage of this muscle memory to improve the typing performance of blind people on wide touch panels. To this end, four input methods are designed, and a prototype for each one is developed. These input methods are compared with each other and with a standard input method. The results of the comparison show that using input methods designed in this report improves typing performance. The most promising and the least promising approaches are specified.

This thesis discusses handwriting as a possible text input method for Virtual Reality (VR) with a goal of comparing handwriting with a virtual keyboard input method. VR applications have different approaches to text input and there is no standard for how the user should enter text. Text input methods are important for the user in many cases, e.g when they document, communicate or enter their login information. The goal of the study was to understand how a handwriting input would compare to pointing at a virtual keyboard, which is the most common approach to the problem. A prototype was built using Tesseract for character recognition and Unity to create a basic virtual environment. This prototype was then evaluated with a user study, comparing it to the de facto standard virtual keyboard input method. The user study had a usability and desirability questionnaire approach and also uses Sutcliffe's heuristics for evaluation of virtual environments. Interviews were performed with each test user. The results suggested that the virtual keyboard performs better except for how engaging the input method was. From the interviews a common comment was that the handwriting input method was more fun and engaging. Further applications of the handwriting input method are discussed as well as why the users favored the virtual keyboard method. / Virtual Reality (VR) applikationer har olika tillvägagångssätt för textinmatning och det finns ingen tydlig standard hur användaren matar in text i VR. Textinmatning är viktigt när användaren ska dokumentera, kommunicera eller logga in. Målet med studien var att jämföra en inmatningsmetod baserad på handskrift med det de facto standard virtuella tangentbordet och se vilken inmatningsmetod användarna föredrog. En prototyp som använde handskrift byggdes med hjälp av Tesseract för textinmatning och Unity för att skapa en virtuell miljö. Prototypen jämfördes sedan med det virtuella tangentbordet i en användarstudie. Användarstudien bestod av uppmätt tid samt antal fel, en enkät och en intervju. Enkäten grundades på användarbarhet, önskvärdhet och Sutcliffes utvärderingsheuristik av virtuella miljöer. Resultatet visar att det virtuella tangentbordet presterade bättre, handskriftsmetoden presterade endast bättre på att engagera användaren. Resultatet från intervjuerna styrkte också att handskriftsmetoden var roligare och mer engagerande att använda men inte lika användbar. Framtida studier föreslås i diskussionen samt varför användarna föredrog det virtuella tangentbordet.