October 1996

Document Type


Degree Name



Dept. of Computer Science and Engineering


Oregon Graduate Institute of Science & Technology


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 [82] 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. This dissertation makes several significant contributions: the design of a simple and linguistically sound language-identification module; a flexible automatic feature extraction algorithm; a mathematical model to estimate the discriminability of two languages; and a mathematical model to capture the impact of inaccurate alignment on the discriminability of two languages.





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