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Learning from the best: A teacher-student multilingual framework for low-resource languages

Citation Author(s):
Deblin Bagchi and William Hartmann
Submitted by:
Deblin Bagchi
Last updated:
13 May 2019 - 5:43pm
Document Type:
Poster
Document Year:
2019
Event:
Presenters Name:
Deblin Bagchi
Paper Code:
SLP-P3.1

Abstract 

Abstract: 

The traditional method of pretraining neural acoustic models in low-resource languages consists of initializing the acoustic model parameters with a large, annotated multilingual corpus and can be a drain on time and resources. In an attempt to reuse TDNN-LSTMs already pre-trained using multilingual training, we have applied Teacher-Student (TS) learning as a method of pretraining to transfer knowledge from a multilingual TDNN-LSTM to a TDNN. The pretraining time is reduced by an order of magnitude with the use of language-specific data during the teacher-student training. Additionally, the TS architecture allows us to leverage untranscribed data, previously untouched during supervised training. The best student TDNN achieves a WER within 1% of the teacher TDNN-LSTM performance and shows consistent improvement in recognition over TDNNs trained using the traditional pipeline over all the evaluation languages. Switching to TDNN from TDNN-LSTM also allows sub-real time decoding.

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Dataset Files

ICASSP_2019_poster_multi_deblin_bagchi

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