Direct exposure of linguistic principles in model architectures for downstream processing

Extensive work on models processing text and speech has led to massively multilingual tools targeting often lower-resourced languages. Examining those tools’ architectures, however, has often revealed little use of specific linguistic principles in a directly explainable fashion, whether in model setup, training, or evaluation. This work seeks to further the inclusion of linguistic information within the structure of systems that process language, whether for text generation or speech recognition. Systems demonstrating differing levels of system restructuring to expose such information include, at one end, a novel text generation system using discrete semantic and syntactic units, built on an open knowledge base and community contributions of code and data. A foundation speech recognizer fine-tuned using morphemic units to handle over-regularized children’s speech serves as the other end of the restructuring spectrum. Following these are improvements to multilingual phone recognition systems through the use and transfer of articulatory information, as a means of imparting some explainability to those systems. Reductions in phone error rates have originated in diversified training corpora to improve language and phone coverage, the introduction of self-supervised waveform input processing, and adjustments to universal phone and feature sets for consistency and brevity. Such efforts highlight the importance of introducing explicit linguistic decompositions, whether phonological, morphological, or syntactic, to models that process language, and the need for continual improvements to sources providing those decompositions.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Morshed, Mahir Abrar

Contributors dc:contributor

• Hasegawa-Johnson, Mark A.
• Hasegawa-Johnson, Mark A
• Smaragdis, Paris
• Tang, Yan
• Singer, Andrew C
• Varshney, Lav R.

• Low-resource Speech Recognition
• Articulatory Feature Detection
• Over-regularized Speech
• Structured Text Generation

Statement dc:rights

• Copyright 2025 Mahir Morshed

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/130021

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F130021