other,70-5-E06-1035,bq |
<term>
cue phrases
</term>
and
<term>
overlapping
|
speech
|
</term>
, are better indicators for the top-level
|
#10597
Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. |
other,44-3-H92-1003,bq |
in a
<term>
multi-site common evaluation of
|
speech
|
, natural language and spoken language
|
#18610
We summarize the motivation for this effort, the goals, the implementation of a multi-site data collection paradigm, and the accomplishments of MADCOW in monitoring the collection and distribution of 12,000 utterances of spontaneous speech from five sites for use in a multi-site common evaluation of speech, natural language and spoken language |
other,26-2-H94-1034,bq |
<term>
likely repair
</term>
or as
<term>
fluent
|
speech
|
</term>
. Other contextual clues , such as
|
#21332
The tagger is given knowledge about category transitions for speechrepairs, and so is able to mark a transition either as a likely repair or as fluent speech. |
other,18-3-H92-1074,bq |
natural grammar
</term>
, and
<term>
spontaneous
|
speech
|
</term>
. This paper presents an overview
|
#19599
The new CSR corpus supports research on major new problems including unlimited vocabulary, natural grammar, and spontaneous speech. |
other,26-1-N01-1003,bq |
syntactic structure
</term>
for elementary
<term>
|
speech
|
acts
</term>
and the decision of how to combine
|
#1319
Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementaryspeech acts and the decision of how to combine them into one or more sentences. |
other,8-1-C04-1103,bq |
important role in many
<term>
multilingual
|
speech
|
and language applications
</term>
. In this
|
#5738
Machine transliteration/back-transliteration plays an important role in many multilingual speech and language applications. |
other,9-1-N03-2003,bq |
language modeling
</term>
of
<term>
conversational
|
speech
|
</term>
are limited . In this paper , we
|
#3024
Sources of training data suitable for language modeling of conversational speech are limited. |
lr,27-3-H90-1060,bq |
than the usual pooling of all the
<term>
|
speech
|
data
</term>
from many
<term>
speakers
</term>
|
#17061
In addition, combination of the training speakers is done by averaging the statistics> of independently trained models rather than the usual pooling of all thespeech data from many speakers prior to training. |
other,8-1-H01-1017,bq |
users in robust ,
<term>
mixed-initiative
|
speech
|
dialogue interactions
</term>
which reach
|
#215
To support engaging human users in robust, mixed-initiative speech dialogue interactions which reach beyond current capabilities in dialogue systems, the DARPA Communicator program [1] is funding the development of a distributed message-passing infrastructure for dialogue systems which all Communicator participants are using. |
lr,27-2-H90-1060,bq |
</term>
, which uses a large amount of
<term>
|
speech
|
</term>
from a few
<term>
speakers
</term>
instead
|
#17015
First, we present a new paradigm for speaker-independent (SI) training of hidden Markov models (HMM), which uses a large amount ofspeech from a few speakers instead of the traditional practice of using a little speech from many speakers. |
other,35-3-H92-1003,bq |
<term>
utterances
</term>
of
<term>
spontaneous
|
speech
|
</term>
from five sites for use in a
<term>
|
#18598
We summarize the motivation for this effort, the goals, the implementation of a multi-site data collection paradigm, and the accomplishments of MADCOW in monitoring the collection and distribution of 12,000 utterances of spontaneous speech from five sites for use in a multi-site common evaluation of speech, natural language and spoken language |
lr,41-2-H90-1060,bq |
traditional practice of using a little
<term>
|
speech
|
</term>
from many
<term>
speakers
</term>
. In
|
#17029
First, we present a new paradigm for speaker-independent (SI) training of hidden Markov models (HMM), which uses a large amount of speech from a few speakers instead of the traditional practice of using a littlespeech from many speakers. |
lr,23-6-H90-1060,bq |
corpus
</term>
and a small amount of
<term>
|
speech
|
</term>
from the new ( target )
<term>
speaker
|
#17142
Second, we show a significant improvement for speaker adaptation (SA) using the new SI corpus and a small amount ofspeech from the new (target) speaker. |
other,12-3-I05-5003,bq |
<term>
PER
</term>
which leverages
<term>
part of
|
speech
|
information
</term>
of the
<term>
words
</term>
|
#8381
We also introduce a novel classification method based on PER which leverages part of speech information of the words contributing to the word matches and non-matches in the sentence. |
other,15-1-H92-1010,bq |
</term><term>
LIMSI
</term>
in the field of
<term>
|
speech
|
processing
</term>
, but also in the related
|
#18632
The paper provides an overview of the research conducted at LIMSI in the field ofspeech processing, but also in the related areas of Human-Machine Communication, including Natural Language Processing, Non Verbal and Multimodal Communication. |
tech,28-1-H92-1095,bq |
<term>
language understanding
</term>
with
<term>
|
speech
|
recognition
</term>
,
<term>
knowledge-based
|
#19665
Language understanding work at Paramax focuses on applying general-purpose language understanding technology to spoken language understanding, text understanding, and document processing, integrating language understanding withspeech recognition, knowledge-based information retrieval and image understanding. |
tech,36-12-J05-1003,bq |
ranking tasks
</term>
, for example ,
<term>
|
speech
|
recognition
</term>
,
<term>
machine translation
|
#8972
Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example,speech recognition, machine translation, or natural language generation. |
other,7-1-H90-1060,bq |
contributions to
<term>
large vocabulary continuous
|
speech
|
recognition
</term>
. First , we present
|
#16985
This paper reports on two contributions to large vocabulary continuous speech recognition. |
tech,16-2-C90-3014,bq |
computational
<term>
phonological system
</term>
:
<term>
|
speech
|
recognition
</term>
and
<term>
synthesis system
|
#16397
The approach of KPSG provides an explicit development model for constructing a computational phonological system:speech recognition and synthesis system. |
tech,17-3-H92-1036,bq |
unified approach for the following four
<term>
|
speech
|
recognition
</term>
applications , namely
|
#19116
Because of its adaptive nature, Bayesian learning serves as a unified approach for the following fourspeech recognition applications, namely parameter smoothing, speaker adaptation, speaker group modeling and corrective training. |