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.