Abstract
Language identification is the task of automatically identifying the language of the speech signal uttered by an unknown speaker. An N language identification task is to classify an input speech utterance, spoken by unknown speaker and of unknown text, as belonging to one of the N languages. LID has applications as a front-end for machines of multi-lingual information retrieval system, multi-lingual speech recognition system and speech to speech translation system. In this paper, hidden Markov model is used for speech recognition and language modeling, i.e., bi-gram model which is the special case of N-gram model (n = 2 for bi-gram). The maximum-likelihood classifier is used to identify the language of given test speech.
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References
Dai Jayram AKV, Ramasubramanian VV, Sreenivas TV (2003) Language identification using parallel phone recognition. In: Proceedings of IEEE International Conference on Acoustics, Speech Signal Processing. pp 32–35
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© 2013 Springer India
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Patil (Hatte J.S), J., Desai, P.K. (2013). Word-Based LID Using HMM and Bi-gram Modeling. In: Chakravarthi, V., Shirur, Y., Prasad, R. (eds) Proceedings of International Conference on VLSI, Communication, Advanced Devices, Signals & Systems and Networking (VCASAN-2013). Lecture Notes in Electrical Engineering, vol 258. Springer, India. https://doi.org/10.1007/978-81-322-1524-0_45
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DOI: https://doi.org/10.1007/978-81-322-1524-0_45
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