39,924 research outputs found
Eye-tracking measurements of language processing: developmental differences in children at high risk for ASD
To explore how being at high risk for autism spectrum disorder (ASD), based on having an older sibling diagnosed with ASD, affects word comprehension and language processing speed, 18-, 24- and 36-month-old children, at high and low risk for ASD were tested in a cross- sectional study, on an eye gaze measure of receptive language that measured how accurately and rapidly the children looked at named target images. There were no significant differences between the high risk ASD group and the low risk control group of 18- and 24-month-olds. However, 36-month-olds in the high risk for ASD group performed significantly worse on the accuracy measure, but not on the speed measure. We propose that the language processing efficiency of the high risk group is not compromised, but other vocabulary acquisition factors might have lead to the high risk 36-month-olds to comprehend significantly fewer nouns on our measure.K01 DC013306 - NIDCD NIH HHS; R01 DC010290 - NIDCD NIH HHS; K01DC013306 - NIDCD NIH HHS; R01 DC 10290 - NIDCD NIH HH
Multilingual Speech Recognition With A Single End-To-End Model
Training a conventional automatic speech recognition (ASR) system to support
multiple languages is challenging because the sub-word unit, lexicon and word
inventories are typically language specific. In contrast, sequence-to-sequence
models are well suited for multilingual ASR because they encapsulate an
acoustic, pronunciation and language model jointly in a single network. In this
work we present a single sequence-to-sequence ASR model trained on 9 different
Indian languages, which have very little overlap in their scripts.
Specifically, we take a union of language-specific grapheme sets and train a
grapheme-based sequence-to-sequence model jointly on data from all languages.
We find that this model, which is not explicitly given any information about
language identity, improves recognition performance by 21% relative compared to
analogous sequence-to-sequence models trained on each language individually. By
modifying the model to accept a language identifier as an additional input
feature, we further improve performance by an additional 7% relative and
eliminate confusion between different languages.Comment: Accepted in ICASSP 201
Recognition times for 54 thousand Dutch words : data from the Dutch crowdsourcing project
We present a new database of Dutch word recognition times for a total of 54 thousand words, called the Dutch Crowdsourcing Project. The data were collected with an Internet vocabulary test. The database is limited to native Dutch speakers. Participants were asked to indicate which words they knew. Their response times were registered, even though the participants were not asked to respond as fast as possible. Still, the response times correlate around .7 with the response times of the Dutch Lexicon Projects for shared words. Also results of virtual experiments indicate that the new response times are a valid addition to the Dutch Lexicon Projects. This not only means that we have useful response times for some 20 thousand extra words, but we now also have data on differences in response latencies as a function of education and age. The new data correspond better to word use in the Netherlands
Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean
A new tightly coupled speech and natural language integration model is
presented for a TDNN-based continuous possibly large vocabulary speech
recognition system for Korean. Unlike popular n-best techniques developed for
integrating mainly HMM-based speech recognition and natural language processing
in a {\em word level}, which is obviously inadequate for morphologically
complex agglutinative languages, our model constructs a spoken language system
based on a {\em morpheme-level} speech and language integration. With this
integration scheme, the spoken Korean processing engine (SKOPE) is designed and
implemented using a TDNN-based diphone recognition module integrated with a
Viterbi-based lexical decoding and symbolic phonological/morphological
co-analysis. Our experiment results show that the speaker-dependent continuous
{\em eojeol} (Korean word) recognition and integrated morphological analysis
can be achieved with over 80.6% success rate directly from speech inputs for
the middle-level vocabularies.Comment: latex source with a4 style, 15 pages, to be published in computer
processing of oriental language journa
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