Ichiro Akahori

A Spoken Dialog Corpus for Car Telematics Services

Spoken corpora provide a critical resource for research, development and evaluation of spoken dialog systems. This chapter describes the spoken dialog corpus used in the design of CAMMIA (Conversational Agent for Multimedia Mobile Information Access), which employs a novel dialog management system that allows users to switch dialog tasks in a flexible manner. The corpus for car telematics services was collected from 137 male and 113 female speakers. The age distribution of speakers is balanced in the five age brackets of 20’s, 30’s, 40’s, 50’s, and 60’s. Analysis of the gathered dialogs reveals that the average number of dialog tasks per speaker was 8.1. The three most frequentlyrequested types of information in the corpus were traffic information, tourist attraction information, and restaurant information. Analysis of speaker utterances shows that the implied vocabulary size is approximately 5,000 words. The results are used for development and evaluation of automatic speech recognition (ASR) and dialog management software.

Robust Multimodal Dialog Management for Mobile Environments

This chapter describes three aspects of mobile dialog management: robustness in the presence of recognition errors; dynamic behavior based on user context (e.g. network connectivity, location); and efficient scenario description for multimodal dialogs. We describe algorithmic techniques for these three aspects of mobile dialog management, and results from empirical user studies are discussed which indicate significant improvement in performance and user satisfaction when these techniques are deployed in a dialog system.