This paper describes a dialog management system called MASCOTS which manages a dialog between a user and a problem solving system through spoken Japanese and helps the speech understanding system in its language processing. MASCOTS tries to predict the next user utterance based on the architecture for managing dialog with two stacks and plan information. MASCOTS not only contributes to making language processing efficient, but also works for a problem solving system. MASCOTS identifies the kind of the utterance and standardizes its representation form in place of a problem solving system. In this paper, the architecture of MASCOTS is discussed focusing on the characteristics of dialog and two ways of predicting the next user utterance exchanging the information with the language processing system.

This paper describes automatic generation of speech synthesis rules which predict a stress level for each bunsetsu in long noun phrases. The rules are inductively inferred from a lot of speech data by using two kinds of tree-based methods, the conventional decision tree and the SBR-tree methods. The rule sets automatically generated by two methods have almost the same performance and decrease the prediction error to about 14 Hz from 23 Hz of the accent component value. The rate of the correct reproduction of the change for adjacent bunsetsu pairs is also used as a measure for evaluating the generated rule sets and they correctly reproduce the change of about 80%. The effectiveness of the rule sets is verified through the listening test. And, with regard to the comprehensiveness of the generated rules, the rules by the SBR-tree methods are very compact and easy to human experts to interpret and matches the former studies.

A speech understanding system confronts with the ambiguities caused by the acoustic-phonetic errors and multiple-meaning of words. Thus the effective framework is required to resolve the ambiguity. The speech understanding system described in this paper deals with two different kind of phrase to avoid the combinatorial explosion. And the speech understanding system is constructed on ATMS based problem solving system to extract maximum performance. Experimental results show that, the time consumed by the speech understanding system reduces into 10. Furthermore, to evaluate the generality and effectiveness of the ATMS based problem solving system, the results of an another experiment are also presented in this paper.

A digital technique for evaluating the half-power band-width of a resonance system is presented. It is based on the lateral shifting operation of the z-transform of the samples sequence on the z-plane. The shifting operation is carried out by using the binomial coefficients.

A time domain formulation for source sound isolation is presented using a new concept of matrix convolution, and is compared with the corresponding frequency domain formulation. Computer simulation suggests that this method is applicable to practical cases.

This paper describes a method for predicting the user's next utterances in spoken dialog based on the topic transition model, named TPN. Some templates are prepared for each utterance pair pattern modeled by SR-plan. They are represented in terms of five kinds of topic-independent constituents in sentences. The topic of an utterance is predicted based on the TPN model and it instantiates the templates. The language processing unit analyzes the speech recognition result using the templates. An experiment shows that the introduction of the TPN model improves the performance of utterance recognition and it drastically reduces the search space of candidates in the input bunsetsu lattice.

This paper shows the best operators for sum-of-products expressions. We first describe conditions of functions for product and sum operations. We examine all two-variable functions and select those that meet the conditions and then evaluate the number of product terms needed in the minimum sum-of-products expressions when each combination of selected product and sum functions is used. As a result of this, we obtain three product functions and nine sum functions on three-valued logic. We show that each of three product functions can express the same functions and MODSUM function is the most suitable for reduction of product terms. Moreover, we show that similar results are obtained on four-valued logic.

The present paper has the following three major objectives:a) To propose a new hierarchical clustering algorithm based on a powerful distance measure defined by using k-nearest neighbor.b) To discuss the validity problem of clustering.c) To describe the need of the constructed data sets for validation of clustering algorithms and to execute the validation of several algorithms including ours using the data sets.Section 2 describes the authors' view of cluster analysis and cluster itself where discussion is made lying stress on the necessity of establishing the concept of a cluster and developing an efficient clustering algorithm free from any restriction associated with the most existing ones. In Sect. 3, a new hierarchical clustering algorithm is proposed which can be considered as a modified version of single-link algorithm. The algorithm is based on a powerful distance measure defined by using the idea of k-nearest neighbor. In Sect. 4, some four-dimensional data for testing the validity of clustering algorithms is presented along with how to construct them. Section 5 describes some results of computer simulation of several clustering algorithms including ours using the constructed data. The results show the efficiency of out algorithm.