Reconstructing False Start Errors in Spontaneous Speech Text

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 255–263, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Reconstructing false start errors in spontaneous speech text Erin Fitzgerald Johns Hopkins University Baltimore, MD, USA erinf@jhu.edu Keith Hall Google, Inc. Zurich, Switzerland kbhall@google.com Frederick Jelinek Johns Hopkins University Baltimore, MD, USA jelinek@jhu.edu Abstract This paper presents a conditional ran- dom field-based approach for identifying speaker-produced disfluencies (i.e. if and where they occur) in spontaneous speech transcripts. We emphasize false start re- gions, which are often missed in cur- rent disfluency identification approaches as they lack lexical or structural similar- ity to the speech immediately following. We find that combining lexical, syntac- tic, and language model-related features with the output of a state-of-the-art disflu- ency identification system improves over- all word-level identification of these and other errors. Improvements are reinforced under a stricter evaluation metric requiring exact matches between cleaned sentences annotator-produced reconstructions, and altogether show promise for general re- construction efforts. 1 Introduction The output of an automatic speech recognition (ASR) system is often not what is required for sub- sequent processing, in part because speakers them- selves often make mistakes (e.g. stuttering, self- correcting, or using filler words). A cleaner speech transcript would allow for more accurate language processing as needed for natural language process- ing tasks such as machine translation and conver- sation summarization which often assume a gram- matical sentence as input. A system would accomplish reconstruction of its spontaneous speech input if its output were to represent, in flawless, fluent, and content- preserving text, the message that the speaker in- tended to convey. Such a system could also be ap- plied not only to spontaneous English speech, but to correct common mistakes made by non-native speakers (Lee and Seneff, 2006), and possibly ex- tended to non-English speaker errors. A key motivation for this work is the hope that a cleaner, reconstructed speech transcript will allow for simpler and more accurate human and natu- ral language processing, as needed for applications like machine translation, question answering, text summarization, and paraphrasing which often as- sume a grammatical sentence as input. This ben- efit has been directly demonstrated for statistical machine translation (SMT). Rao et al. (2007) gave evidence that simple disfluency removal from tran- scripts can improve BLEU (a standard SMT eval- uation metric) up to 8% for sentences with disflu- encies. The presence of disfluencies were found to hurt SMT in two ways: making utterances longer without adding semantic content (and sometimes adding false content) and exacerbating the data mismatch between the spontaneous input and the clean text training data. While full speech reconstruction would likely require a range of string transformations and po- tentially deep syntactic and semantic analysis of the errorful text (Fitzgerald, 2009), in this work we will first attempt to resolve less complex errors, corrected by deletion alone, in a given manually- transcribed utterance. We build on efforts from (Johnson et al., 2004), aiming to …

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