Physical Sciences and Mathematics Commons^™

Adaptation And Augmentation: Towards Better Rescoring Strategies For Automatic Speech Recognition And Spoken Term Detection, Min Ma

Dissertations, Theses, and Capstone Projects

Selecting the best prediction from a set of candidates is an essential problem for many spoken language processing tasks, including automatic speech recognition (ASR) and spoken keyword spotting (KWS). Generally, the selection is determined by a confidence score assigned to each candidate. Calibrating these confidence scores (i.e., rescoring them) could make better selections and improve the system performance. This dissertation focuses on using tailored language models to rescore ASR hypotheses as well as keyword search results for ASR-based KWS.

This dissertation introduces three kinds of rescoring techniques: (1) Freezing most model parameters while fine-tuning the output layer in order to …