Sixth International Conference on Spoken Language Processing (ICSLP 2000)
October 16-20, 2000
Stochastic Modeling of Semantic Content for Use IN A Spoken Dialogue System
Magne H. Johnsen (1), Trym Holter (2), Torbjørn Svendsen (1), Erik Harborg (2)
(1) Norwegian University of Science and Technology (NTNU),
(2) SINTEF Telecom and Informatics, Trondheim, Norway
A key issue in a spoken dialogue system is the successful semantic
interpretation of the output from the speech recognizer. Extracting
the semantic concepts, i.e. the meaningful phrases, of an
utterance is traditionally performed using rule based methods. In
this paper we describe a statistical framework for modeling (and
decoding) semantic concepts based on discrete hidden Markov
models (DHMMs). Each semantic concept class is modeled as
a multi-state DHMM, where the observations are the recognized
words. The proposed decoding procedure is capable of parsing an
utterance into a sequence of phrases, each belonging to a different
concept class. The phrase sequence will correspond to a concept
segmentation and class identification, whilst the semantic entities
constituting each phrase contain the semantic value.
The algorithm has been tested on a dialogue system for bus route
information in Norwegian. The results confirm the applicability
of the procedure. Semantically relevant concepts in input inquiries
could be identified with 6.9% error rate on the sentence
level. The corresponding segmentation error rate was 8.6% when
concept segmentation information was available during training.
Without this information, i.e. if the training was performed in an
embedded mode, the segmentation error rate increased to 23.5%.
Johnsen, Magne H. / Holter, Trym / Svendsen, Torbjørn / Harborg, Erik (2000):
"Stochastic modeling of semantic content for use IN a spoken dialogue system",
In ICSLP-2000, vol.1, 218-221.