15,505 research outputs found
Semantic Processing of Out-Of-Vocabulary Words in a Spoken Dialogue System
One of the most important causes of failure in spoken dialogue systems is
usually neglected: the problem of words that are not covered by the system's
vocabulary (out-of-vocabulary or OOV words). In this paper a methodology is
described for the detection, classification and processing of OOV words in an
automatic train timetable information system. The various extensions that had
to be effected on the different modules of the system are reported, resulting
in the design of appropriate dialogue strategies, as are encouraging evaluation
results on the new versions of the word recogniser and the linguistic
processor.Comment: 4 pages, 2 eps figures, requires LaTeX2e, uses eurospeech.sty and
epsfi
Robust Processing of Natural Language
Previous approaches to robustness in natural language processing usually
treat deviant input by relaxing grammatical constraints whenever a successful
analysis cannot be provided by ``normal'' means. This schema implies, that
error detection always comes prior to error handling, a behaviour which hardly
can compete with its human model, where many erroneous situations are treated
without even noticing them.
The paper analyses the necessary preconditions for achieving a higher degree
of robustness in natural language processing and suggests a quite different
approach based on a procedure for structural disambiguation. It not only offers
the possibility to cope with robustness issues in a more natural way but
eventually might be suited to accommodate quite different aspects of robust
behaviour within a single framework.Comment: 16 pages, LaTeX, uses pstricks.sty, pstricks.tex, pstricks.pro,
pst-node.sty, pst-node.tex, pst-node.pro. To appear in: Proc. KI-95, 19th
German Conference on Artificial Intelligence, Bielefeld (Germany), Lecture
Notes in Computer Science, Springer 199
World-view perspectives
The foundation of a tolerant society is an ability to foster and
respond to the diversity of perspective among its people. Cognitive
psychologists have described how perspective influences information
processing, while our innate ability to adopt perspective has been established
by neuropsychology. Literature, through the use of point-of-view, together
with results from researchers adopting socio-cultural paradigms suggests
perspective is also a social construct. An ecologically-based framework is
described that provides cohesion to the temporal, spatial, universal and other
types of world-view perspective associated, predominantly, with indigenous
cultures. Culturally responsible types of creative and critical thinking are
evoked when world-view perspective is engaged while reading text and
reading the world. World-view perspective provides us with a means of
critiquing the construction of knowledge through the de-construction of
dominant discourses, re-valuing of indigenous world-views and reducing the
relational distance between indigenous and non-indigenous peoples
Leveraging native language information for improved accented speech recognition
Recognition of accented speech is a long-standing challenge for automatic
speech recognition (ASR) systems, given the increasing worldwide population of
bi-lingual speakers with English as their second language. If we consider
foreign-accented speech as an interpolation of the native language (L1) and
English (L2), using a model that can simultaneously address both languages
would perform better at the acoustic level for accented speech. In this study,
we explore how an end-to-end recurrent neural network (RNN) trained system with
English and native languages (Spanish and Indian languages) could leverage data
of native languages to improve performance for accented English speech. To this
end, we examine pre-training with native languages, as well as multi-task
learning (MTL) in which the main task is trained with native English and the
secondary task is trained with Spanish or Indian Languages. We show that the
proposed MTL model performs better than the pre-training approach and
outperforms a baseline model trained simply with English data. We suggest a new
setting for MTL in which the secondary task is trained with both English and
the native language, using the same output set. This proposed scenario yields
better performance with +11.95% and +17.55% character error rate gains over
baseline for Hispanic and Indian accents, respectively.Comment: Accepted at Interspeech 201
Technology assessment of advanced automation for space missions
Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology
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