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Proceedings Paper

Automatic Learning Of Fuzzy Production Rules For Stop Sound In Continuous Speech
Author(s): G. Ippolito; L. Saitta
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Paper Abstract

Phoneme hypothesization in continuous speech can be a difficult task, especially if speaker-independence should be achieved. In general, the interpretation of speech patterns involves the generation of hypotheses concerning possible phonemic transcription of syllable segments automatically extracted from a numerical representation of energy-time-frequency obtained by short-term spectral analysis of a spoken sentence. Each hypothesis is evaluated and a degree of worthiness is assigned to it in such a way that it can be further processed for hypothesizing words or syntactic or semantic structures of the sentence. Moreover, context dependencies among adjacent phonemes must be taken into account; for this purpose, segments of the order of the syllables are considered; in this way the context dependencies (at least in Italian) may only occurr within the speech unit selected. In this paper the method used for the recognition of stop sounds (/b/, /d/, /g/, /p/, /t/, /k/) is described; the system is organized as an expert system, in which various sources of knowledge cooperate. In particular, each expert contains a set of production rules, describing how the different phonemic hypotheses are related to phonetic or acoustic features.

Paper Details

Date Published: 14 June 1984
PDF: 7 pages
Proc. SPIE 0485, Applications of Artificial Intelligence I, (14 June 1984); doi: 10.1117/12.943170
Show Author Affiliations
G. Ippolito, University di Torino (Italy)
L. Saitta, University di Torino (Italy)


Published in SPIE Proceedings Vol. 0485:
Applications of Artificial Intelligence I
John F. Gilmore, Editor(s)

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