On a system that captures spoken dialog, users often use out-of-domain utterances to the system. The speech recognition component in the dialog system cannot correctly recognize such utterances, which causes fatal errors. This paper proposes a method to verify whether utterances are in-domain or out-of-domain. The proposed method trains systems with two language models: one that can accept both in-domain and out-of-domain utterances and the other that can accept only in-domain utterances. These models are installed into two speech recognition systems. A comparison of the recognizers' outputs provides a good verification of utterances. We installed our method in a hospital appointment system and evaluated it. The experimental results showed that the proposed method worked well.

This paper examines and reports on how ”filled pauses” included when delivering speeches influence the understanding, and change the impression, of the speech as shown through the research and experiments we conducted on trial subjects. We conducted research about speeches and lectures given at classes at our university, and at academic meetings. A questionnaire related to filled pauses was given to audiences in university classrooms, and the speeches given where recorded. Then, we prepared a number of speeches that were manually altered to put emphasis on the frequency, position, and duration of filled pauses in the speeches. Comparing those speeches with the original speeches which were not processed in our listening experiments, we were able to estimate the effect of filled pauses in a lecture speech and how effective these were in altering the impressions of the audiences. We were able to find the best conditions related to the frequency, position, and duration of filled pauses, and how these conditions cleary changed a lecture or speech into a better one which is easy to understanding and listen to for the audience. Index Terms: Spoken language, Lecture speech, Evaluation of lectures, Filled pause

In both written language and spoken language, we sometimes use different words in order to express the same meaning. For instance, we use “candidacy” and “running in an election” as the same meaning. This makes text classification and event tracking difficult. To do this, we have to identify the words which are semantically similar to each other accurately. In this paper, we propose a method to extract the words which are semantically similar to other words. Using the method, we extracted similar word pairs on newspaper articles. Further, we extracted news articles which are related to a news article. By using a similar word pair list, we obtained better result than that without it. The results suggest that the method is useful for extracting related document, text tracking and so on.

A speech recognition system for continuously spoken Japanese sentences is described. The system has a knowledge source, in which syntactic, semantic and vocabulary knowledge are contained. It can treat not only simple sentences but also simple complex and compound sentences without any restriction regarding the tasks. Phoneme identification is performed in real time, and after a slight revision has been carried out the speech sound is represented by a pseudo-phoneme string. In the syntactic knowledge system, inflections of inflectional words are taken into account. The syntactic analyzer, using a syntactic state transition network, predicts the following words and outputs their syntactic interpretations. The semantic knowledge system deals with the meanings of each word, in particular, it deals with case structures for verbs. The semantic knowledge is used for the prediction of the following words and construction of a semantic network of the input story. Moreover, a slightly modified version can be used as a speech input device of a word processor.