Languages of India

Languages spoken in the Republic of India belong to several language families, the major ones being the Indo-Aryan languages spoken by 78.05% of Indians and the Dravidian languages spoken by 19.64% of Indians; both families together are sometimes known as Indic languages. Languages spoken by the remaining 2.31% of the population belong to the Austroasiatic, Sino–Tibetan, Tai–Kadai, and a few other minor language families and isolates. According to the People's Linguistic Survey of India, India has the second highest number of languages (780), after Papua New Guinea (840). Ethnologue lists a lower number of 456. Article 343 of the Constitution of India stated that the official language of the Union is Hindi in Devanagari script, with official use of English to continue for 15 years from 1947. Later, a constitutional amendment, The Official Languages Act, 1963, allowed for the continuation of English alongside Hindi in the Indian government indefinitely until legislation decides to chan

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Multilingual bottleneck features for subword modeling in zero-resource languages

Enno Hermann

How can we effectively develop speech technology for languages where no transcribed data is available? Many existing approaches use no annotated resources at all, yet it makes sense to leverage information from large annotated corpora in other languages, for example in the form of multilingual bottleneck features (BNFs) obtained from a supervised speech recognition system. In this work, we evaluate the benefits of BNFs for subword modeling (feature extraction) in six unseen languages on a word discrimination task. First we establish a strong unsupervised baseline by combining two existing methods: vocal tract length normalisation (VTLN) and the correspondence autoencoder (cAE). We then show that BNFs trained on a single language already beat this baseline; including up to 10 languages results in additional improvements which cannot be matched by just adding more data from a single language. Finally, we show that the cAE can improve further on the BNFs if high-quality same-word pairs are available.

2018

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The objective of this course is to present the main models, formalisms and algorithms necessary for the development of applications in the field of natural language information processing. The concepts introduced during the lectures will be applied during practical sessions.

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