14,991 research outputs found
Development of an integrated low-power RF partial discharge detector
This paper presents the results from integrating a low-power partial discharge detector with a wireless sensor node designed for operating as part of an IEEE 802.15.4 sensor network, and applying an on-line classifier capable of classifying partial discharges in real-time. Such a system is of benefit to monitoring engineers as it provides a means to exploit the RF technique using a low-cost device while circumventing the need for any additional cabling associated with new condition monitoring systems. The detector uses a frequency-based technique to differentiate between multiple defects, and has been integrated with a SunSPOT wireless sensor node hosting an agent-based monitoring platform, which includes a data capture agent and rule induction agent trained using experimental data. The results of laboratory system verification are discussed, and the requirements for a fully robust and flexible system are outlined
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
We describe a statistical approach for modeling dialogue acts in
conversational speech, i.e., speech-act-like units such as Statement, Question,
Backchannel, Agreement, Disagreement, and Apology. Our model detects and
predicts dialogue acts based on lexical, collocational, and prosodic cues, as
well as on the discourse coherence of the dialogue act sequence. The dialogue
model is based on treating the discourse structure of a conversation as a
hidden Markov model and the individual dialogue acts as observations emanating
from the model states. Constraints on the likely sequence of dialogue acts are
modeled via a dialogue act n-gram. The statistical dialogue grammar is combined
with word n-grams, decision trees, and neural networks modeling the
idiosyncratic lexical and prosodic manifestations of each dialogue act. We
develop a probabilistic integration of speech recognition with dialogue
modeling, to improve both speech recognition and dialogue act classification
accuracy. Models are trained and evaluated using a large hand-labeled database
of 1,155 conversations from the Switchboard corpus of spontaneous
human-to-human telephone speech. We achieved good dialogue act labeling
accuracy (65% based on errorful, automatically recognized words and prosody,
and 71% based on word transcripts, compared to a chance baseline accuracy of
35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling
changed
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