Spoken monologues feature greater sentence length and structural complexity than do spoken dialogues. To achieve high parsing performance for spoken monologues, it could prove effective to simplify the structure by dividing a sentence into suitable language units. This paper proposes a method for dependency parsing of Japanese monologues based on sentence segmentation. In this method, the dependency parsing is executed in two stages: at the clause level and the sentence level. First, the dependencies within a clause are identified by dividing a sentence into clauses and executing stochastic dependency parsing for each clause. Next, the dependencies over clause boundaries are identified stochastically, and the dependency structure of the entire sentence is thus completed. An experiment using a spoken monologue corpus shows this method to be effective for efficient dependency parsing of Japanese monologue sentences.

In applications of spoken monologue processing such as simultaneous machine interpretation and real-time captions generation, incremental language parsing is strongly required. This paper proposes a technique for incremental dependency parsing of Japanese spoken monologue on a clause-by-clause basis. The technique identifies the clauses based on clause boundaries analysis, analyzes the dependency structures of them, and tries to decide the dependency relations with another clauses, simultaneously with the monologue speech input. The dependency relations are generated at the stage before the input of the entire monologue, and therefore, our technique can be used for language parsing in simultaneous Japanese speech understanding. An experiment using Japanese monologues has shown that our technique had the same degree of the performance as the usual dependency parsing for monologue sentences.

This paper describes the design, analysis and utilization of a simultaneous interpretation corpus. The corpus has been constructed at the Center for Integrated Acoustic Information Research (CIAIR) of Nagoya University in order to promote the realization of the multi-lingual communication supporting environment. The size of transcribed data is about 1 million words, and the corpus would deserve to be called the simultaneous interpretation corpus of the largest-in-the-world class. The discourse tag and the utterance time tag were given to the corpus, and some software tools for corpus analysis in order to support the practical use of the corpus have been developed. Therefore, the corpus is expected to be useful not only for the development of simultaneous interpreting systems but also for the construction of an interpreting theory.

Here, we propose a system named Azim that provides service based on both location and direction, which uses position estimation method based on azimuth data. In this system, a user's position is estimated by having the user point to and measure azimuths of several markers or objects whose positions are already known. Because the measurements are naturally associated with some degree of error, the user's position is calculated as a probability distribution. Since both the user's position and azimuth data are obtained in this method, both sources of data are used to realize more advanced services such as identifying the object pointed to by a user. The proposed system utilizes a wireless LAN for supporting these advanced services. Finally, a prototype system was implemented using a direction sensor that combines a magnetic compass and accelerometer, and we exemplify the usefulness of our approach through an experiment.

Abstract This paper proposes a method of speech intention understand-ing based on a spoken dialogue corpus to which the intentiontags are given. The intention tag expresses the task-dependentintention of the speaker, and therefore, the proper understand-ing enables a spoken dialogue system to take appropriate ac-tions. We have tagged about 35000 utterances in the CIAIR in-car speech database. In our method, several decision trees forintention understanding are constructed. By constructing deci-sion trees and using them at the same time, the strong amount ofcharacteristic features related to intentions can be retrieved, andit can also be robustly coped with the diversity of the utterances.An experiment on inference of utterance intentions has shown73.1% accuracy. 1. Introduction In order to interact with a user naturally and smoothly, it is nec-essary for a spoken dialogue system to understand the intentionof the user exactly. As a method of speech intention under-standing, example-based approaches have been considered sofar [1, 3, 6]!%In general, these approaches involve comparinga spoken utterance with examples in a correctly-tagged corpus.The intention of the utterance is regarded as the intention tag ofthe most similar example in the corpus. However, it is difficultto infer the intention of the utterance to which any example inthe corpus is not similar.This paper proposes a method of speech intention under-standing based on a spoken dialogue corpus. The method con-structs several decision trees for intention understanding. Byconstructing several decision trees, the strong amount of char-acteristic features related to intentions can be retrieved, and itcan also be robustly coped with the diversity of the utterances.So far, we have designed an organization of the tags which iscalled Layered Intention Tag(LIT). These tags show more de-tailed utterance intention rather than the illocutionary act level,and have built the corpus[2, 3]. LIT is divided into several lay-ers considering the relevance between an intention and variousphenomena relevant to an utterance, such as a style, a keyword,a sentence structure. This method constructs several decisiontrees by using this corpus and infers the intention by combiningthem.In order to evaluate the effectiveness of our method, an ex-periment on inference of the utterance intentions was conductedusing the driver utterances about restaurant search recorded ona large-scale in-car spoken dialogue corpus of CIAIR[4, 5]. Asa result, the effectiveness of the method was confirmed.

In this paper, we propose a new method to expand an examplebased spoken dialogue system to handle context dependent utterances. The dialogue system refers to the dialogue examples to find an example that is suitable to promote dialogue. Here, the dialogue contexts are expressed in the form of dialogue slots. By constructing dialogue examples with the text of utterances and the dialogue slots, the system handle context dependent dialogue. And we also propose a new framework of spoken dialogue, named “GROW architecture” that consists of the dialogue system and a Wizard-of-OZ (WOZ) system. By using the WOZ system to add dialogue examples via network, it becomes efficient to augment dialogue examples.

Grant-in-Aids for Young Scientists of the Ministry of Education, Science, Sports and Culture, Japan; The Tatematsu Foundation

This paper describes an advanced spoken language corpus which has been constructed by enhancing an in-car speech database. The corpus has the following characteristic features: (1) series Advanced tag: Not only linguistic phenomena tags but also advanced discourse tags such as sentential structures, and utterance intentions, have been provided for the transcribed texts. (2) series Large-scale: The sentential structures and the intentions are currently provided for 45,053 phrases and 35,421 utterance units, respectively. (3) series Multi-layer: The corpus consists of different levels of spoken language data such as speech signals, transcribed texts, sentential structures, intentional markers and dialogue structures, moreover, they are related with each other. It allows a very wide variety of analysis of spontaneous spoken dialogue to utilize the multi-layered corpus. This paper also reports the result of investigation of the corpus, especially, focusing on the relations between the syntactic style and the intentional style of spoken utterances.

The spread of short distance wireless communication technology is making it possible for various information appliances to communicate through a network. However, to connect and to use them, we must generally perform complicated setup operations such as setting addresses or names. In this paper, we propose a user-friendly connecting management framework called " Touch-and-Connect". With this method, we can connect any networked devices only by touching them. This system is designed so that it can work without a server and even if many individuals operate it independently, no incorrect connections occur. And we have exemplified the usefulness of this framework with subject experiments.

Japanese and Uighur languages are agglutinative languages and they have many syntactical and morphological similarities. And roughly speaking, we can translate Japanese into Uighur sequentially by replacing Japanese words with corresponding Uighur ones after morphological analysis. However, we should translate agglutinated suffixes carefully to make correct translation, because they play important roles on both languages. In this paper, we pay attention to them and propose a Japanese-Uighur machine translation utilizing the agglutinative features of both languages. To deal with the agglutinative features, we use the derivational grammar, which makes the similarities clearer between both languages. This makes our system proposed here simple and systematical. We have implemented the machine translation system and evaluated how effectively our system works.

The paper proposes an effective incremental parsing method for such interactive natural language processing systems as real-time dialogue systems, simultaneous machine interpreting systems, etc. This method produces the analysis of the input while it is being received. It can efficiently deal with not only the normal input, which is piecemeal addition to the input from left to right, but also such changes of the input as insertion and deletion. For such changes of the input, this method exploits parts of the previous analyses. We implemented this method on a workstation and conducted an experiment. We confirm that the method can be expected to be useful for an online language processing system.

This paper describes a database consisting of speech and language, which we are currently constructing for the purpose of the research on machine interpretation. The database contains bilingual data of lectures and dialogues. We have collected the speech of about 72 hours in total and transcribed it into the text manually. We have investigated the database in order to acquire empirical knowledge of human interpreting. In this paper, we report the characteristic features of spoken language by Japanese-to-English interpreters.

Towards a real-time spoken dialogue system, several incremental parsing methods have been proposed so far. They constructs syntactic structures for an initial fragment of an input sentence. However, they have a problem that the structures do not necessarily represent the syntactic relation correctly. The problem is caused by the ambiguity of initial fragments. This paper proposes an incremental disambiguation method, which decides correct structures at an stage where the entire input is not completed. The method finds the structures which are correct independently of the remaining input. When correct structures cannot be decided, the method delays the decision. Since the disambiguation is executed for every word input, the method can find correct structures at an early stage.

The Center for Integrated Acoustic Information Research (CIAIR) at Nagoya University has been collecting speech corpora in moving cars which are made available as resources to advance the research and development of robust ASRs and spoken dialogue systems under high-noise conditions. The speech corpus consists of (1) phonetically balanced sentences, (2) digit strings, (3) discrete words and (4) transcribed spoken dialogues between drivers and information systems for navigation and information retrieval. These data are collected in vehicles under both idling and driving situations. The language of the corpus is currently Japanese. The number of subjects is currently about 300, total recording time is over 200 hours and total corpus size is about 160GByte. We have also been recording video images from three different angles, vehicle-control signals, and vehicle location, all synchronized with the speech recording. We report the objective of the speech corpus, the recording methods and the recording vehicle developed.

“Leaf-size” (or “branching”) is the minimum number of leaves of some accepting computation trees of alternating devices. For example, one leaf corresponds to nondeterministic computation. In this paper, we investigate the effect of constant leaves of two-dimensional alternating Turing machines, and show the following facts: (1) For any function L(m, n), k leaf- and L(m, n) space-bounded two-dimensional alternating Turing machines which have only universal states are equivalent to the same space bounded deterministic Turing machines for any integer k≥1, where m (n) is the number of rows (columns) of the rectangular input tapes. (2) For square input tapes, k+1 leaf- and o(log m) space-bounded two-dimensional alternating Turing machines are more powerful than k leaf-bounded ones for each k≥1. (3) The necessary and sufficient space for three-way deterministic Turing machines to simulate k leaf-bounded two-dimensional alternating finite automata is nk+1, where we restrict the space function of three-way deterministic Turing machines to depend only on the number of columns of the given input tapes.

Paz et al. [1] have revealed the necessary and sufficient conditions for a linear automaton to be strongly approximable. In this paper, we extend their theory to a linear space automaton [2], i.e., a linear system with more than one linear mapping, to obtain a necessary and sufficient condition that it is strongly stable.