The challenge of spoken language systems: Research directions for the nineties

Abstract

A spoken language system combines speech recognition, natural language processing and h h a n interface technology. It functions by recognizing the pervn's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate respinse back to the user. Potential applications of spoken lan 8e"systems range from simple tasks, such as retrieving informgo frdm an existing database (traffic reports, airline schedules),$to interactive problem solving tasks involving complex planning and reasoning (travel planning, traflic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: 1) robust speech recognition; 2) automatic training and adaptation; 3) spontaneous speech; 4) dialogue models; 5) natural language response generation; 6) speech synthesis and speech generation; 7) multilingual systems; and 8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and for rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area.

The role of voice input for human-machine communication.

“…Still, it is not currently known: * How to select relatively "closed" domains, for which the vocabulary and linguistic constructs can be acquired through iterative training and testing on a large corpus of user input * How well users can discern the system's communicative capabilities * How well users can stay within the bounds of those capabilities * What level of task performance users can attain * What level of misinterpretation users will tolerate, and what levels of recognition and understanding are needed for them to solve problems effectively * How much and what kind of training may be acceptable Systems are not adept at handling linguistic coverage problems, other than responding that given words are not in the vocabulary, or that the utterance was not understood. Even recognizing that an out-of-vocabulary word has occurred is itself a difficult issue (54). If users can discern the system's vocabulary, one can be optimistic that they can adapt to that vocabulary.…”

Adaptive Network Based Fuzzy Inference System For Speech Recognition Through Subtractive Clustering

“…Therefore, a point would have a height potential value if it has more neighbor points close to itself. 3. the point with the highest potential value is selected as le first cluster center: First cluster center x c1 is chosen as the point having the largest density value D c1 4.…”

“…Many algorithms and schemes based on different mathematical paradigms have been proposed in an attempt to improve recognition rates [3,4,5,6].…”

Speechreading using Probabilistic Models

“…Classi cation is performed by estimating the maximum a p osteriori probability (MAP) arg max i P( i jO) (12) where i represents the model of word class i and O the observation sequence. The a p osteriori probability can be obtained using Bayes rule ,…”