Rochester Institute of Technology RIT Scholar Works Theses 4-2021 Automatic Speech Recognition for Low-Resource and Morphologically Complex Languages Ethan Morris
[email protected] Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Morris, Ethan, "Automatic Speech Recognition for Low-Resource and Morphologically Complex Languages" (2021). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact
[email protected]. Automatic Speech Recognition for Low-Resource and Morphologically Complex Languages Ethan Morris Automatic Speech Recognition for Low-Resource and Morphologically Complex Languages Ethan Morris April 2021 A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Engineering Department of Computer Engineering Automatic Speech Recognition for Low-Resource and Morphologically Complex Languages Ethan Morris Committee Approval: Emily Prud'hommeaux Advisor Date Department of Computer Science, Boston College Alexander Loui Date Department of Computer Engineering Andreas Savakis Date Department of Computer Engineering i Acknowledgments I would like to thank Dr. Emily Prud'hommeaux for her continued support and guidance, with invaluable suggestions. I am also thankful for Dr. Alexander Loui and Dr. Andreas Savakis for being on the thesis committee. I also wish to thank Dr. Raymond Ptucha, Dr. Christopher Homan, and Robert Jimerson for their ideas, advice, and general expertise. ii Abstract The application of deep neural networks to the task of acoustic modeling for au- tomatic speech recognition (ASR) has resulted in dramatic decreases of word error rates, allowing for the use of this technology in smart phones and personal home assistants in high-resource languages.