Deployment of human-machine dialogue systems.

Abstract

The deployment of systems for human-tomachine communication by voice requires overcoming a variety of obstacles that affect the speech-processing technologies. Problems encountered in the field might include variation in speaking style, acoustic noise, ambiguity of language, or confusion on the part of the speaker. The diversity of these practical problems encountered in the "real world" leads to the perceived gap between laboratory and "real-world" performance. To answer the question "What applications can speech technology support today?" the concept of the "degree of difficulty" of an application is introduced. The degree of difficulty depends not only on the demands placed on the speech recognition and speech synthesis technologies but also on the expectations of the user of the system. Experience has shown that deployment of effective speech communication systems requires an iterative process. This paper discusses general deployment principles, which are illustrated by several examples of human-machine communication systems.Speech-processing technology is now at the point at which people can engage in voice dialogues with machines, at least in limited ways. Simple voice communication with machines is now deployed in personal computers, in the automation of long-distance calls, and in voice dialing of mobile telephones. These systems have small vocabularies and strictly circumscribed task domains. In research laboratories there are advanced human-machine dialogue systems with vocabularies of thousands of words and intelligence to carry on a conversation on specific topics. Despite these successes, it is clear that the truly intelligent systems envisioned in science fiction are still far in the future, given the state of the art today.Human-machine dialogue systems can be represented as a four-step process, as shown in Fig. 1. This figure encompasses both the simple systems deployed today and the spoken language understanding we envision for the future. First, a speech recognizer transcribes sentences spoken by a person into written text (1, 2). Second, a language understanding module extracts the meaning from the text (3, 4). Third, a computer (consisting of a processor and a database) performs some action based on the meaning of what was said. Fourth, the person receives feedback from the computer in the form of a voice created by a speech synthesizer (5, 6). The boundaries between these stages of a dialogue system may not be distinct in practice. For instance, language-understanding modules may have to cope with errors in the text from the speech recognizer, and the speech recognizer may make use of grammar and semantic constraints from the language module in order to reduce recognition errors.In the 1993 "Colloquium on Human-Machine Communication by Voice," sponsored by the National Academy of Sciences (NAS), much of the discussion focused on practical difficulties in building and deploying systems for carrying on voice dialogues between humans and machines. Deployment of systems for human-to-mach...

Voice interactive classroom, a service-oriented software architecture for speech-enabled learning

“…The effectiveness of speech recognition is related to factors such as the speaker's independence, his/her expertise, confusability of the speaker's vocabulary and grammar, speaking mode (rate and coarticulation), channel conditions and user tolerance of errors (Roe, 1995). At the same time, the demands of using speech rather than keyboard entry may slow speech users more in the highercognitive-load task of composition (Shneiderman, 2000).…”