Accession Number : ADA325601
Title : Automatic Language Identification with Sequences of Language-Independent Phoneme Clusters.
Descriptive Note : Final rept., 1 Sep 93-31 Aug 96,
Corporate Author : ARMY FOREIGN SCIENCE AND TECHNOLOGY CENTER CHARLOTTESVILLE VA
Personal Author(s) : Berkling, Kay M.
PDF Url : ADA325601
Report Date : AUG 1996
Pagination or Media Count : 164
Abstract : Automatic Language Identification involves analyzing language specific features in speech to determine the language of an utterance without regard to topic, speaker or length of speech. Although much progress has been made in recent years, language identification systems have not been built on detailed underlying theory or linguistically meaningful design criteria. This thesis is motivated by the belief that features used to discriminate between languages should be linguistically sound; the result is a unique combination of design, theory and implementation. In this thesis a word-spotting algorithm is introduced motivated by a perceptual study reporting that human subjects use language dependent phonemes and short sequences to identify languages. In order to find an optimal set of phoneme-like tokens to represent speech in a linguistically meaningful way, a mathematical model of the discrimination between two languages is developed. This model permits the automatic design of a token representation of speech by selecting a list of discriminating words in a data-driven manner. The resulting system has the flexibility to automatically take into account the inherent structure of the languages to be discriminated. A second mathematical model is developed to measure the impact of inaccurate automatic alignment of tokens on language discrimination. This model indicates why some algorithms aiming to compensate for these inaccuracies have not been successful. The theoretical models and the word-spotting algorithm have been implemented and validated on both generated and real-world speech data.
Descriptors : *NEURAL NETS, *SPEECH RECOGNITION, *WORD RECOGNITION, *PHONEMES, DATA BASES, MATHEMATICAL MODELS, ALGORITHMS, OPTIMIZATION, DATA MANAGEMENT, COMPUTER AIDED DESIGN, MAXIMUM LIKELIHOOD ESTIMATION, PATTERN RECOGNITION, SPEECH ANALYSIS, GERMAN LANGUAGE, ACOUSTIC DATA, COMPUTATIONAL LINGUISTICS, SPEECH REPRESENTATION, ENGLISH LANGUAGE.
Subject Categories : Linguistics
Distribution Statement : APPROVED FOR PUBLIC RELEASE