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 [BibTeX] [Marc21]
Fast Language Adaptation Using Phonological Information
Type of publication: Conference paper
Citation: Tong_INTERSPEECH_2018
Publication status: Published
Booktitle: Proceedings of Interspeech 2018
Year: 2018
Pages: 2459-2463
Location: Hyderabad, INDIA
ISSN: 2308-457X
ISBN: 978-1-5108-7221-9
DOI: 10.21437/Interspeech.2018-1990
Abstract: Phoneme-based multilingual connectionist temporal classification (CTC) model is easily extensible to a new language by concatenating parameters of the new phonemes to the output layer. In the present paper, we improve cross-lingual adaptation in the context of phoneme-based CTC models by using phonological information. A universal (IPA) phoneme classifier is first trained on phonological features generated from a phonological attribute detector. When adapting the multilingual CTC to a new, never seen, language, phonological attributes of the unseen phonemes are derived based on phonology and fed into the phoneme classifier. Posteriors given by the classifier are used to initialize the parameters of the unseen phonemes when extending the multilingual CTC output layer to the target language. Adaptation experiments show that the proposed initialization approaches further improve the cross-lingual adaptation on CTC models and yield significant improvements over Deep Neural Network / Hidden Markov Model (DNN/HMM)-based adaptation using limited data.
Keywords: connectionist temporal classification (ctc), crosslingual adaptation, dnn-based speech recognition, Phonological features
Projects SUMMA
Authors Tong, Sibo
Garner, Philip N.
Bourlard, Hervé
Added by: [UNK]
Total mark: 0
  • Tong_INTERSPEECH_2018.pdf