Browse Theses

The focus of this thesis is to improve the ability of a computational system to understand spoken utterances in a dialogue with a human. Available computational methods for word recognition do not perform as well on spontaneous speech in task-oriented dialogue as we would hope. Even a state of the art recognizer achieves slightly worse than 70% word accuracy on spontaneous speech in a conversation focused on solving a specific problem.

To address this problem, I explore novel methods for post-processing the output of a speech recognizer in order to correct errors. I adopt statistical techniques for modeling the noisy channel from the speaker to die listener in order to correct some of the errors introduced there. The statistical model accounts for frequent errors such as simple word/word confusions and short phrasal problems (one-to-many word substitutions and many-to-one word concatenations). To use the model, a search algorithm is employed to find the most likely correction of a given word sequence from the speech recognizer. The post-processor output contains fewer errors, thus making interpretation by downstream components, such as parsing, more reliable.

The post-processor was employed in the T RAINS -95 and T RAINS -96 conversational planning assistants to great avail. Using these techniques, we were able to reduce the number of word recognition errors in some scenarios by approximately 17% (absolute) in the T RAINS -95 and T RAINS -96 systems (from just under 40% to nearly 20%). Consequently, both systems were significantly more robust to recognition errors when using the post-processor than when not. In the scenario where the speech recognizer is tunable with the availability of new data, the impact of these techniques is not as large, but they do make an improvement nonetheless.