A Phonotactic Language Model for Spoken Language Identification

Proceedings of the 43rd Annual Meeting of the ACL, pages 515–522, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics A Phonotactic Language Model for Spoken Language Identification Haizhou Li and Bin Ma Institute for Infocomm Research Singapore 119613 {hli,mabin}@i2r.a-star.edu.sg Abstract We have established a phonotactic lan- guage model as the solution to spoken language identification (LID). In this framework, we define a single set of acoustic tokens to represent the acoustic activities in the world’s spoken languages. A voice tokenizer converts a spoken document into a text-like document of acoustic tokens. Thus a spoken document can be represented by a count vector of acoustic tokens and token n-grams in the vector space. We apply latent semantic analysis to the vectors, in the same way that it is applied in information retrieval, in order to capture salient phonotactics present in spoken documents. The vector space modeling of spoken utterances con- stitutes a paradigm shift in LID technol- ogy and has proven to be very successful. It presents a 12.4% error rate reduction over one of the best reported results on the 1996 NIST Language Recognition Evaluation database. 1 Introduction Spoken language and written language are similar in many ways. Therefore, much of the research in spoken language identification, LID, has been in- spired by text-categorization methodology. Both text and voice are generated from language de- pendent vocabulary. For example, both can be seen as stochastic time-sequences corrupted by a chan- nel noise. The n-gram language model has achieved equal amounts of success in both tasks, e.g. n-character slice for text categorization by lan- guage (Cavnar and Trenkle, 1994) and Phone Rec- ognition followed by n-gram Language Modeling, or PRLM (Zissman, 1996) . Orthographic forms of language, ranging from Latin alphabet to Cyrillic script to Chinese charac- ters, are far more unique to the language than their phonetic counterparts. From the speech production point of view, thousands of spoken languages from all over the world are phonetically articulated us- ing only a few hundred distinctive sounds or pho- nemes (Hieronymus, 1994). In other words, common sounds are shared considerably across different spoken languages. In addition, spoken documents1, in the form of digitized wave files, are far less structured than written documents and need to be treated with techniques that go beyond the bounds of written language. All of this makes the identification of spoken language based on pho- netic units much more challenging than the identi- fication of written language. In fact, the challenge of LID is inter-disciplinary, involving digital signal processing, speech recognition and natural lan- guage processing. In general, a LID system usually has three fun- damental components as follows: 1) A voice tokenizer which segments incoming voice feature frames and associates the seg- ments with acoustic or phonetic labels, called tokens; 2) A statistical language model which captures language dependent phonetic and phonotactic information from the sequences of tokens; 3) A language classifier which identifies the lan- guage based on discriminatory characteristics of acoustic score from the voice …

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