Dealing with linguistic mismatches for automatic speech recognition

Recent breakthroughs in automatic speech recognition (ASR) have resulted in a word error rate (WER) on par with human transcribers on the English Switchboard benchmark. However, dealing with linguistic mismatches between the training and testing data is still a significant challenge that remains unsolved. Under the monolingual environment, it is well-known that the performance of ASR systems degrades significantly when presented with the speech from speakers with different accents, dialects, and speaking styles than those encountered during system training. Under the multi-lingual environment, ASR systems trained on a source language achieve even worse performance when tested on another target language because of mismatches in terms of the number of phonemes, lexical ambiguity, and power of phonotactic constraints provided by phone-level n-grams. In order to address the issues of linguistic mismatches for current ASR systems, my dissertation investigates both knowledge-gnostic and knowledge-agnostic solutions. In the first part, classic theories relevant to acoustics and articulatory phonetics that present capability of being transferred across a dialect continuum from local dialects to another standardized language are re-visited. Experiments demonstrate the potentials that acoustic correlates in the vicinity of landmarks could help to build a bridge for dealing with mismatches across difference local or global varieties in a dialect continuum. In the second part, we design an end-to-end acoustic modeling approach based on connectionist temporal classification loss and propose to link the training of acoustics and accent altogether in a manner similar to the learning process in human speech perception. This joint model not only performed well on ASR with multiple accents but also boosted accuracies of accent identification task in comparison to separately-trained models.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Informatics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Yang, Xuesong

Contributors dc:contributor

• Hasegawa-Johnson, Mark
• Huang, Thomas S.
• Smaragdis, Paris
• Shih, Chilin

• Automatic Speech Recognition
• Acoustic Modeling
• Multi-Accents
• Multi-Lingual
• Acoustic Phonetics
• Distinctive Features
• Acoustic Landmarks
• End-to-End
• Multi-Task Learning
• Model Compression
• Deep Learning
• Pronunciation Error Detection
• Connectionist Temporal Classification

Statement dc:rights

• Copyright 2019 Xuesong Yang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/105187

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/105187