Robust, Finite-State Parsing for Spoken Language Understanding

Robust, Finite-State Parsing for Spoken Language Understanding Edward C. Kaiser Center for Spoken Language Understanding Oregon Graduate Institute PO Box 91000 Portland OR 97291 kaiser©cse, ogi. edu Abstract Human understanding of spoken language ap- pears to integrate the use of contextual ex- pectations with acoustic level perception in a tightly-coupled, sequential fashion. Yet com- puter speech understanding systems typically pass the transcript produced by a speech rec- ognizer into a natural language parser with no integration of acoustic and grammatical con- straints. One reason for this is the complex- ity of implementing that integration. To ad- dress this issue we have created a robust, se- mantic parser as a single finite-state machine (FSM). As such, its run-time action is less com- plex than other robust parsers that are based on either chart or generalized left-right (GLR) architectures. Therefore, we believe it is ul- timately more amenable to direct integration with a speech decoder. 1 Introduction An important goal in speech processing is to ex- tract meaningful information: in this, the task is understanding rather than transcription. For extracting meaning from spontaneous speech full coverage grammars tend to be too brittle. In the 1992 DARPA ATIS task competition, CMU's Phoenix parser was the best scoring sys- tem (Issar and Ward, 1993). Phoenix operates in a loosely-coupled architecture on the 1-best transcript produced by the recognizer. Concep- tually it is a semantic case-frame parser (Hayes et al., 1986). As such, it allows slots within a particular ease-frame to be filled in any order, and allows out-of-grammar words between slots to be skipped over. Thus it can return partial parses -- as frames in which only some of the available slots have been filled. Humans appear to perform robust under- standing in a tightly-coupled fashion. They build incremental, partial analyses of an ut- terance as it is being spoken, in a way that helps them to meaningfully interpret the acous- tic evidence. To move toward machine under- standing systems that tightly-couple acoustic features and structural knowledge, researchers like Pereira and Wright (1997) have argued for the use of finite-state acceptors (FSAs) as an efficient means of integrating structural knowl- edge into the recognition process for limited do- main tasks. We have constructed a parser for spontaneous speech that is at once both robust and finite- state. It is called PROFER, for Predictive, RO- bust, Finite-state parsER. Currently PROFER accepts a transcript as input. We are modifying it to accept a word-graph as input. Our aim is to incorporate PROFER directly into a recog- nizer. For example, using a grammar that defines se- quences of numbers (each of which is less than ten thousand and greater than ninety-nine and contains the word "hundred"), inputs like the following string can be robustly parsed by PRO- FER: Input: first I've got twenty ahhh thirty yaaaaaa thirty ohh wait no twenty twenty nine hundred two errr three ahhh four and then two hundred ninety uhhhhh let me be sure here yaaaa ninety seven and last is five …

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