Low resource automatic intonation classification using gated recurrent unit (GRU) networks pre-trained with synthesized pitch patterns

Saha, A and Yarra, C and Ghosh, PK (2019) Low resource automatic intonation classification using gated recurrent unit (GRU) networks pre-trained with synthesized pitch patterns. In: 20th Annual Conference of the International Speech Communication Association: Crossroads of Speech and Language, INTERSPEECH 2019, 15 - 19 September 2019, Graz, pp. 959-963.

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Abstract

Second language learners of British English (BE) are typically trained to learn four intonation classes - Glide-up, Glide-down, Dive and Take-off. We predict the intonation class in a learner's utterance by modeling the temporal dependencies in the pitch patterns with gated recurrent unit (GRU) networks. For these, we pre-train the GRU network using a set of synthesized pitch patterns representing each intonation class. For the synthesis, we propose to obtain pitch patterns from the tone sequences representing each intonation class obtained from domain knowledge. Experiments are conducted on speech data collected from experts in a spoken English training material for teaching BE intonation. The absolute improvements in the unweighted average recall (UAR) using the proposed scheme with pre-training are found to be 4.14 and 6.01 respectively over the proposed approach without pre-training and the baseline scheme that uses hidden Markov models (HMMs).

Item Type:	Conference Paper
Publication:	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publisher:	International Speech Communication Association
Additional Information:	The copyright for this article belongs to International Speech Communication Association.
Keywords:	Computer assisted language learning; Intonation classification; LSTM with pre-training; Synthetic pitch for intonation
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	05 Dec 2022 09:33
Last Modified:	05 Dec 2022 09:33
URI:	https://eprints.iisc.ac.in/id/eprint/78249

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