Cross-lingual articulation information transfer with progressive networks for speech recognition

Morshed, Mahir

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https://hdl.handle.net/2142/115607 Copy

Description

Title

Cross-lingual articulation information transfer with progressive networks for speech recognition

Author(s)

Morshed, Mahir

Issue Date

2022-04-29

Director of Research (if dissertation) or Advisor (if thesis)

Hasegawa-Johnson, Mark A

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• articulatory feature detection
• transfer learning
• progressive neural networks

Abstract

A system for the lateral transfer of information from recurrent end-to-end neural networks recognizing articulatory feature classes to similarly structured networks recognizing phone tokens is proposed. The system uses connections between recurrent layers at the same depth between feature detectors pre-trained on a base language and a phone recognizer for different target languages, inspired primarily by the progressive neural network scheme. While initial experiments using detectors for four articulatory feature classes—consonant place, consonant manner, vowel height, and vowel backness—trained on Bengali speech, attached to phone recognizers for four other Asian languages (Javanese, Nepali, Sinhalese, Sundanese), do not currently suggest consistent performance improvements across different low-resource settings for target languages, irrespective of their genealogic or phonological relatedness to Bengali, they do suggest the need for further trials with different language sets, altered data sources and data configurations, and slightly altered network setups.

Graduation Semester

2022-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Mahir Morshed

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