Development of an approach to automatic language identification based on phone recognition

Barnard, Etienne; Cole, Ronald A.

doi:10.1006/csla.1996.0003

Cited by 22 publications

(16 citation statements)

References 15 publications

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“…[10] successfully used phonotactic-acoustic features. Later Yan [9] applied a comb ination of acoustic, phonotactic and prosodic information for language identification. Nagawaka [8] co mpared four different methods to identify languages and concluded that continuous hidden Markov model (HMM) based method works best.…”

Section: Existing Approachesmentioning

confidence: 99%

Mixed Language Speech Recognition without Explicit Identification of Language

Bhuvanagirir¹,

Kopparapu

2012

AJSP

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Use of mixed language in day to day spoken speech is becoming common and is accepted as being syntactically correct. Ho wever mach ine recognition of mixed language spoken speech is a challenge to a conventional speech recognition engine. There are studies on how to enable recognition of mixed language speech. At one end of the spectra is to use acoustic models of the co mplete phone set of the mixed language to enable recognition wh ile on the other end of the spectra is to use a language identification module followed by language dependent speech recognition engines to do the recognition. Each of this has its own imp licat ions. In this paper, we approach the problem of mixed language speech recognition by using available resources and show that by suitably constructing an appropriate pronunciation dictionary and modifying the language model to use mixed language, one can achieve a good recognition accuracy of spoken mixed language.

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Section: Existing Approachesmentioning

confidence: 99%

Mixed Language Speech Recognition without Explicit Identification of Language

Bhuvanagirir¹,

Kopparapu

2012

AJSP

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“…an English phone recognizer. Zissman [51] and Yan [48] Mendoza [30], Schultz [43,44] and Hieronymus [16] have have extended this work to systems containing multiple,…”

Section: Language Identification Systemsmentioning

confidence: 99%

Automatic Language Identification

Torres-Carrasquillo¹,

Zissman²

2007

Wiley Encyclopedia of Electrical and Electronics Engineering

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“…In the statistical sense, phonotactics can be viewed as a subset of grammatical and lexical rules of a language, whose discriminancy is naturally reflected in the phonotactic properties. Several contributions were published dealing with the use of phone -grams, particularly bigrams, for phonotactic classification of languages [3]- [6] showing superior performance as compared to purely acoustic or prosodic systems. Various configurations of multiple language-dependent phone-recognizers, run in parallel, were designed to represent different phone repertories of the languages and to improve the performance of simple phonotactic components [3], [4].…”

Section: Language Characteristicsmentioning

confidence: 99%

Spoken language recognition-a step toward multilinguality in speech processing

Navrátil

2001

IEEE Trans. Speech Audio Process.

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In recent years, automatic recognition of spoken languages has become an important feature in a variety of speech-enabled multilingual applications which, besides accuracy, also demand for efficient and "linguistically scalable" algorithms. This paper deals with a particularly successful approach based on phonotactic-acoustic features and presents systems for language identification as well as for unknown-language rejection. An architecture with multipath decoding, improved phonotactic models using binary-tree structures, and acoustic pronunciation models serve as a framework for experiments and discussion on these two tasks. In particular, language identification accuracy on a telephone-speech task (NIST'95 evaluation) in six and nine languages is presented together with results from a perceptual experiment carried out with human listeners. The performance of language rejection based on phonotactic modeling combined with a monolingual LVCSR system in the domain of broadcast news transcription is also reported. Besides yielding state-of-the-art performance, the described systems are computationally inexpensive and easily extensible (scalable) to new languages without the need for linguistic experts.

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Development of an approach to automatic language identification based on phone recognition

Cited by 22 publications

References 15 publications

Mixed Language Speech Recognition without Explicit Identification of Language

Mixed Language Speech Recognition without Explicit Identification of Language

Automatic Language Identification

Spoken language recognition-a step toward multilinguality in speech processing

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