This paper presents an analysis of named entity recognition and classification in spontaneous speech transcripts. We annotated a significant fraction of the Switchboard corpus with six named entity classes and investigated a battery of machine learning models that include lexical, syntactic, and semantic attributes. The best recognition and classification model obtains promising results, approa...

This paper describes new language resources designed to support research in speaker recognition. It begins with a brief overview of collections protocols, motivates the shift from the Switchboard protocol to the Mixer protocol, summarizes yields from the earliest phase of Mixer collection and then describes more recent phases, yields and expected yields and lessons learned.

We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NPand non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron’s (2002) manually tuned system.

Using corpus data of spoken dialogue, we examine the convergence of syntactic complexity levels between interlocutors in natural conversations, as it occurs within spans of topic episodes. The findings of general convergence in the Switchboard and BNC corpora are compatible with an information-theoretic model of dialogue and with Interactive Alignment Theory.

This paper investigates the prosodic characteristics of reported speech in the Switchboard corpus. We find that directly reported speech is signalled by a greater overall pitch range than the surrounding narrative material and is typically preceded by intonational phrase boundaries. By contrast, prosody does not seem to distinguish indirectly reported speech from ordinary narrative speech. The ...

This paper describes a syntactic representation for modeling speech repairs. This representation makes use of a right corner transform of syntax trees to produce a tree representation in which speech repairs require very few special syntax rules, making better use of training data. PCFGs trained on syntax trees using this model achieve high accuracy on the standard Switchboard parsing task.

Rapid adaptation schemes that employ the EM algorithm may suffer from overtraining problems when used with small amounts of adaptation data. An algorithm to alleviate this problem is derived within the information geometric framework of Csiszár and Tusnády, and is used to improve MLLR adaptation on NAB and Switchboard adaptation tasks. It is shown how this algorithm approximately optimizes a di...