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Multi-Source Neural Model for Machine Translation of Agglutinative Language
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In: Future Internet ; Volume 12 ; Issue 6 (2020)
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| 2 |
A High Efficient Biological Language Model for Predicting Protein–Protein Interactions
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In: Cells ; Volume 8 ; Issue 2 (2019)
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Punctuation and Parallel Corpus Based Word Embedding Model for Low-Resource Languages
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In: Information ; Volume 11 ; Issue 1 (2019)
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Constructing Uyghur Commonsense Knowledge Base by Knowledge Projection
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In: Applied Sciences ; Volume 9 ; Issue 16 (2019)
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| 5 |
The ingredients of comparison: The semantics of the excessive construction in Japanese
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In: Semantics and Pragmatics, Vol 8, Iss 0, Pp 1-38 (2015) (2015)
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Machine Learning Paradigms for Speech Recognition: An Overview
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| 7 |
Sequence Clustering and Labeling for Unsupervised Query Intent Discovery
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In: http://www.cs.utoronto.ca/%7Ejcheung/papers/wsdm2012.pdf (2012)
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| 8 |
Understanding the semantic structure of noun phrase queries
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In: http://research.microsoft.com/pubs/130815/acl.pdf (2010)
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Semi-supervised learning of semantic classes for query . . .
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In: http://research.microsoft.com/pubs/101154/fp0894-wang-webpost.pdf (2009)
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| 10 |
Rejoinder: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
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| 11 |
The Vocal Joystick data collection effort and vowel corpus
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In: http://ssli.ee.washington.edu/people/bilmes/mypapers/VJ_ICSLP_2006_v8.pdf (2006)
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Abstract:
Vocal Joystick is a mechanism that enables individuals with motor impairments to make use of vocal parameters to control objects on a computer screen (buttons, sliders, etc.) and ultimately will be used to control electro-mechanical instruments (e.g., robotic arms, wireless home automation devices). In an effort to train the VJ-system, speech data from the TIMIT speech corpus was initially used. However, due to problematic issues with co-articulation, we began a large data collection effort in a controlled environment that would not only address the problematic issues, but also yield a new vowel corpus that was representative of the utterances a user of the VJ-system would use. The data collection process evolved over the course of the effort as new parameters were added and as factors relating to the quality of the collected data in terms of the specified parameters were considered. The result of the data collection effort is a vowel corpus of approximately 11 hours of recorded data comprised of approximately 23500 sound files of the monophthongs and vowel combinations (e.g. diphthongs) chosen for the Vocal Joystick project varying along the parameters of duration, intensity and amplitude. This paper discusses how the data collection has evolved since its initiation and provides a brief summary of the resulting corpus. Index Terms: Speech corpora, data collection procedures, speech recognition, Speech HCI for individuals with impairments, Speech/voice-based human-computer interfaces 1.
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URL: http://ssli.ee.washington.edu/people/bilmes/mypapers/VJ_ICSLP_2006_v8.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.78.5823
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| 12 |
The Vocal Joystick: A voice-based humancomputer interface for individuals with motor impairments
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In: https://www.ee.washington.edu/techsite/papers/documents/UWEETR-2005-0007.pdf (2005)
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| 13 |
The Vocal Joystick Demo at UIST05: A Voice-Based Human-Computer Interface
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In: http://ssli.ee.washington.edu/vj/files/UIST-demo-abstract.pdf (2005)
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| 14 |
Algorithms for data-driven ASR parameter quantization
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In: http://ssli.ee.washington.edu/people/bilmes/mypapers/quan-algo-csl-sdarticle.pdf (2005)
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| 15 |
The Vocal Joystick: A Voice-Based Human-Computer Interface for Individuals with Motor Impairments
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In: http://ssli.ee.washington.edu/people/bilmes/mypapers/uist05.pdf (2005)
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| 16 |
Algorithms for Data-Driven ASR Parameter Quantization
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In: http://ssli.ee.washington.edu/people/karim/papers/quan-algorithms.pdf (2003)
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| 17 |
A Phrase Table Filtering Model Based on Binary Classification for Uyghur-Chinese Machine Translation
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In: http://www.jcomputers.us/vol9/jcp0912-02.pdf
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| 18 |
General Terms
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In: http://dub.washington.edu/pubs/assets2006/assets71-harada.pdf
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| 19 |
LEXICON MODELING FOR QUERY UNDERSTANDING
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In: http://groups.csail.mit.edu/sls/publications/2011/Liu_ICASSP2011.pdf
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| 20 |
Understanding the semantic structure of noun phrase queries. ACL’10
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In: http://aclweb.org/anthology-new/P/P10/P10-1136.pdf
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