Knowledge-based and expert systems in automatic speech recognition

Artificial Intelligence (AI) has recently advanced to the point that practical applications are now existing in several domains. Most of the results obtained are not due to general problem solving techniques but to the use of specific, domain-dependent knowledge. Formalizing and incorporating specific knowledge into a system makes it possible to reach the level of expertise comparable to that of a human expert in some specialized field. Such knowledge-based and expert systems have been extensively used in various domains like chemistry, medicine, geology, etc. The basic idea in these systems is to clearly distinguish between the knowledge base which usually incorporates rules and meta-rules about the domain of expertise and the control structures which manipulate this knowledge. That ensures great modularity and flexibility and makes it easy to modify and update a system [11].

[1]  J. Baker,et al.  The DRAGON system--An overview , 1975 .

[2]  Randall Davis,et al.  An overview of production systems , 1975 .

[3]  Victor Lesser,et al.  Organization of the Hearsay II speech understanding system , 1975 .

[4]  Edward H. Shortliffe,et al.  Chapter 3 – Consultation System , 1976 .

[5]  Victor Lesser,et al.  IN THE HEARSAY-II SPEECH UNDERSTANDING SYSTEM , 1976 .

[6]  Edward H. Shortliffe,et al.  Computer-based medical consultations, MYCIN , 1976 .

[7]  Allen R. Hanson,et al.  Segmentation Processes in the VISIONS System , 1977, IJCAI.

[8]  Gerald J. Sussman,et al.  Forward Reasoning and Dependency-Directed Backtracking in a System for Computer-Aided Circuit Analysis , 1976, Artif. Intell..

[9]  Franklin S. Cooper,et al.  Speech Understanding Systems , 1976, Artificial Intelligence.

[10]  Ronald A. Cole,et al.  Experiments on spectrogram reading , 1979, ICASSP.

[11]  Victor R. Lesser,et al.  The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty , 1980, CSUR.

[12]  Frederick Hayes-Roth,et al.  Building expert systems , 1983, Advanced book program.

[13]  S. Ross,et al.  A speech spectrogram expert , 1983, ICASSP.

[14]  Pietro Laface,et al.  Phonetic feature hypothesization in continuous speech , 1983, ICASSP.

[15]  Victor Zue,et al.  Selecting acoustic features for stop consonant identification , 1983, ICASSP.

[16]  D. Huttenlocher,et al.  Exploring phonotactic lexical constraints in word recognition , 1983 .

[17]  Lalit R. Bahl,et al.  A Maximum Likelihood Approach to Continuous Speech Recognition , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Renato De Mori.  Extraction of acoustic cues using a grammar of frames , 1983, Speech Commun..

[19]  Renato De Mori,et al.  Computer Models of Speech Using Fuzzy Algorithms , 1983, Advanced Applications in Pattern Recognition.

[20]  N. Carbonell,et al.  An expert system for the automatic reading of French spectrograms , 1984, ICASSP.

[21]  Jean-François Mari,et al.  Some experiments in automatic recognition of a thousand word vocabulary , 1984, ICASSP.

[22]  Jacqueline Vaissière,et al.  A new rule-based expert system for speech recognition , 1987 .