Although topic transition has been studied in dialogue for decades, only a handful of corpora based quantitative studies have been conducted to investigate the nature of topic transitions. Towards this end, this study annotates 215 conversations from the switchboard corpus, perform quantitative analysis and finds that 1) longer conversations consists of more topic transitions, 2) topic transition are usually lead by one participant and 3) we found no pattern in time series progression of topic transition. We also model topic transition with a precision of 91%.

Casual conversation has become a focus for artificial dialogue applications. Such talk is ubiquitous and its structure differs from that found in the task-based interactions which have been the focus of dialogue system design for many years. It is unlikely that such conversations can be modelled as an extension of task-based talk. We review theories of casual conversation, report on our studies of the structure of casual dialogue, and outline challenges we see for the development of spoken dialog systems capable of carrying on casual friendly conversation in addition to performing well-defined tasks.

Though a number of web-based CAT tools have emerged over recent years, to date the most common form of CAT tool used by translators remains the desktop-based CAT tool. However, currently none of the most commonly used desktop-based CAT tools provide a means of measuring translation speed at a segment level. This metric is important, as previous work on MT productivity testing has shown that edit distance can be a misleading measure of MT post-editing effort. In this paper we present iOmegaT, an instrumented version of a popular desktop-based open-source CAT tool called OmegaT. We survey a number of similar applications and outline some of the weaknesses of web-based CAT tools for experi- enced professional translators. On the basis of a two productivity test carried out using iOmegaT we show why it is important to be able to identify fast good post-editors to maximize MT utility and how this is problematic using only edit-distance measures. Finally, we argue how and why instrumentation could be added to more commonly used desktop-based CAT tools that are paid for by freelance translators if their privacy is respected.

This paper describes our English Speech-to-Text (STT) systems for the 2012 IWSLT TED ASR track evaluation. The systems consist of 10 subsystems that are combinations of different front-ends, e.g. MVDR based and MFCC based ones, and two different phone sets. The outputs of the subsystems are combined via confusion network combination. Decoding is done in two stages, where the systems of the second stage are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cM-LLR.

This paper describes the KIT-NAIST (Contrastive) English speech recognition system for the IWSLT 2012 Evaluation Campaign. In particular, we participated in the ASR track of the IWSLT TED task. The system was developed by Karlsruhe Institute of Technology (KIT) and Nara Institute of Science and Technology (NAIST) teams in collaboration within the interACT project. We employ single system decoding with fully continuous and semi-continuous models, as well as a three-stage, multipass system combination framework built with the Janus Recognition Toolkit. On the IWSLT 2010 test set our single system introduced in this work achieves a WER of 17.6%, and our final combination achieves a WER of 14.4%.

This paper describes our English Speech-to-Text (STT) system for the 2011 IWSLT ASR track. The system consists of 2 subsystems with different front-ends—one MVDR based, one MFCC based—which are combined using confusion network combination to provide a base for a second pass speaker adapted MVDR system. We demonstrate that this set-up produces competitive results on the IWSLT 2010 dev and test sets.

This paper describes our current Spanish speech-to-text (STT) system with which we participated in the 2011 Quaero STT evaluation that is being developed within the Quaero program. The system consists of 4 separate subsystems, as well as the standard MFCC and MVDR phoneme based subsystems we included a both a phoneme and grapheme based bottleneck subsystem. We carefully evaluate the performance of each subsystem. After including several new techniques we were able to reduce the WER by over 30% from 20.79% to 14.53%.