Automatic language identification

TL;DR: This paper attempts to provide an introductory tutorial on the fundamentals of the theory and the state-of-the-art solutions of spoken language recognition, from both phonological and computational aspects.

TL;DR: It is demonstrated how approaches to language identification based on acoustic modeling and language modeling are similar to algorithms used in speaker-independent continuous speech recognition.

TL;DR: In this article, a system and method for improving the accuracy of audio searching using multiple models to process an audio file or stream to obtain search tracks is presented, where search tracks are processed to locate at least one search term and generate multiple search results.

TL;DR: This tutorial presents an overview of the progression of spoken language identification (LID) systems and current developments, and Evaluations of the LID system are presented using NIST language recognition evaluation tasks.

TL;DR: The structure of language and identity, child language acquisition, language, brain and handicap, and language and communication are discussed.

TL;DR: Four approaches for automatic language identification of speech utterances are compared: Gaussian mixture model (GMM) classification; single-language phone recognition followed by languaged dependent, interpolated n-gram language modeling (PRLM); parallel PRLM, which uses multiple single- language phone recognizers, each trained in a different language; and languagedependent parallel phone recognition (PPR).

TL;DR: This 1000 plus page reference work would certainly be a useful and impressive acquisition to any linguist's bookshelf and is a veritable mine of knowledge concerning language knowledge.