David Attwater

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A recent trial of natural language call steering on live UK calls to the operator is described along with its results. The characteristics of the problem are described along with the acoustic, language, semantic and dialogue modelling approaches employed. Natural language call steering is found to be viable, with recognition and semantic accuracy the(More)
OASIS is a research project at BT Labs investigating practical large-scale spoken language automation of call steering (call routing). BT’s own operator assistance service is used as an initial trial domain. Spoken language call steering requires understanding of both user’s language and behaviour. Therefore, the OASIS project makes extensive use of(More)
In this paper, a dialogue system for natural language based call steering is described and studied. The system is based on natural language speech recognition and understanding within a mixed initiative dialogue. The system is implemented on Bell Labs. Speech Technology Integration Platform (BLSTIP) using dialogue and natural language understanding(More)
Almost every speech application involves integration with real world databases which may be large or complex. Telephony based examples include call-centre automation, customer identification and directory assistance. Many such applications are intrinsically large vocabulary problems with complex data requirements. This paper illustrates the architectural,(More)
Today’s automated telephone services generally use recorded speech from one speaker for all output. In applications with large and varying output vocabularies, such as place names, it may be necessary to employ a second speaker to provide new vocabulary items if the original speaker is not available, or to use text-tospeech (TTS) synthesis for the whole or(More)
“How may I help you?” systems where a caller to a call centre is routed to one of a set of destinations using machine recognition of spontaneous natural language is a difficult task. Previous BT “How May I Help You” work [1,2] has used top 1 recognition results for classification with much better results when tested on human transcriptions. Classifying(More)
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