A Speech-First Model for Repair Detection and Correction

A SPEECH-FIRST MODEL FOR REPAIR DETECTION AND CORRECTION Christine Nakatani Division of Applied Sciences Harvard University Cambridge MA 02138 ABSTRACT Interpreting fttUy natural speech is an important goal for spoken language understanding systems. However, while corpus studies have shown that about 10% of spontaneous utterances contain self- corrections, or REPAIRS, little is known about the extent to which cues in the speech signal may facilitate repair processing. We identify several cues based on acoustic and prosodic analysis of repairs in the DARPA Air Travel In.formation System database, and propose methods for exploiting these cues to detect and correct repairs. 1. INTRODUCTION Disfluencies in spontaneous speech pose serious problems for spoken language Systems. First, a speaker may produce a partial word or FRAGMEtCr, a string of phonemes that does not form the complete word intended by the speaker. Some fragments may coincidentally match words actually in the lexicon, as in (1); others will be identified with the acoustically closest lexicon item(s), as in (2). 1 (1) What is the earliest fli- flight from Washington to Atlanta leaving on Wednesday September fourth? (2) Actual string: What is the fare fro-- on American Airlines fourteen forty three Recognized string: With fare four American Airlines fourteen forty three Even if all words in a disfluent segment are correctly recog- nized, failure to detect the location of a disfluency may lead to interpretation errors during subsequent processing, as in (3): (3) ... Delta leaving Boston seventeen twenty one arriving Fort Worth twenty two twenty one forty and flight num- ber... Here, 'twenty two twenty one forty' must somehow be inter- preted as a flight arrival time; the system must choose on some basis among .21.40, 22.21, and . . . 22:40'. IWe indicate the presence of a word fragment in examples by the dia- critic '-'. Self-corrected portions of the utterance, or REI'ARANDA, appear in boldface. Unless otherwise noted, all repair examples ia this paper are drawn from the corpus described in Section 4. Recognizer output shown is from the recognition system described in [1] on the ATIS lune 1990 test. Julia Hirschberg 2D-450, AT&T Bell Laboratories 600 Mountain Avenue Murray Hill NJ 07974-0636 Although studies of large speech corpora have found that approximately 10% of spontaneous utterances contain disflu- encies involving self-correction, or REPArRS [2, 3], little is known about how to integrate repair processing with real- time speech recognition and with incremental syntactic and semantic analysis of partial utterances in spoken language systems. In particular, the speech signal itself has been rel- atively unexplored as a source of processing cues that may facilitate the detection and correction of repairs. In this paper, we present results from a pilot study examining the acous- tic and prosodic characteristics of all repairs (146) occurring in 1,453 utterances from the DARPA Air Travel Information System (ATIS) database. Our results are interpreted within a new "speech-first" framework for investigating repairs, the REPAIR INTERVAL MODEL, which builds upon Labov 1966 [4] and Hindle 1983 [2]. 2. PREVIOUS COMPUTATIONAL APPROACKES …

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