TR 592: A Robust Loose Coupling
for Speech Recognition and Natural Language Understanding

Please download a copy of the paper by clicking here: Thesis Proposal in gzip'ed PostScript format (237K)

Abstract:

The focus of this thesis proposal 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 as we would hope. Even a state of the art recognizer achieves slightly worse than 70% word accuracy on (nearly) spontaneous speech in a conversation about a specific problem.

To address this problem, I will 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 the 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 required to find the most likely correction of a given word sequence from the speech recognizer. The post-processor output should contain fewer errors, thus making interpretation by higher levels, such as parsing, more reliable.

Spontaneous speech is also challenging to process because it is more incremental than written language. Utterances frequently form brief phrases and fragments rather than full sentences; they tend to come in installments and refinements. Known methods for parsing do not perform as well as we would like in the face of these linguistic ambiguities and idiosyncrasies. Even state of the art algorithms for parsing spontaneous language sustain high error rates.

To address the incrementality of spontaneously spoken utterances, I will develop methods for segmenting a given utterance into ``chunks'' representing individual thoughts. Given an utterance of spontaneous speech, a tool for automatic prosodic feature extraction will analyze the output of the error-correcting post-processor and the acoustic waveform to generate prosodic cues. These cues will aid a robust parser using a prosody-wise grammar to identify the incremental phrases in the utterance and to provide a syntactic analysis.

These components will augment the TRAINS-95 conversational planning assistant.

Keywords: Dialogue, Prosody, Spoken Language Understanding, Spontaneous Speech, the TRAINS-95 System.

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