  In this thesis, we present a statistical language model for resolving speech
repairs, intonational boundaries and discourse markers. Rather than finding the
best word interpretation for an acoustic signal, we redefine the speech
recognition problem to so that it also identifies the POS tags, discourse
markers, speech repairs and intonational phrase endings (a major cue in
determining utterance units). Adding these extra elements to the speech
recognition problem actually allows it to better predict the words involved,
since we are able to make use of the predictions of boundary tones, discourse
markers and speech repairs to better account for what word will occur next.
Furthermore, we can take advantage of acoustic information, such as silence
information, which tends to co-occur with speech repairs and intonational
phrase endings, that current language models can only regard as noise in the
acoustic signal. The output of this language model is a much fuller account of
the speaker's turn, with part-of-speech assigned to each word, intonation
phrase endings and discourse markers identified, and speech repairs detected
and corrected. In fact, the identification of the intonational phrase endings,
discourse markers, and resolution of the speech repairs allows the speech
recognizer to model the speaker's utterances, rather than simply the words
involved, and thus it can return a more meaningful analysis of the speaker's
turn for later processing.
