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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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| 6 |
Machine Learning Paradigms for Speech Recognition: An Overview
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Abstract:
Automatic Speech Recognition (ASR) has historically been a driving force behind many machine learning (ML) techniques, including the ubiquitously used hidden Markov model, discriminative learning, structured sequence learning, Bayesian learning, and adaptive learning. Moreover, ML can and occasionally does use ASR as a large-scale, realistic application to rigorously test the effectiveness of a given technique, and to inspire new problems arising from the inherently sequential and dynamic nature of speech. On the other hand, even though ASR is available commercially for some applications, it is largely an unsolved problem—for almost all applications, the performance of ASR is not on par with human performance. New insight from modern ML methodology shows great promise to advance the state-of-the-art in ASR technology. This overview article provides readers with an overview of modern ML techniques as utilized in the current and as relevant to future ASR research and systems. The intent is to foster further cross-pollination between the ML and ASR communities than has occurred in the past. The article is organized according to the major ML paradigms that are either popular already or have potential for making significant contributions to ASR technology. The paradigms presented and elaborated in this overview include: generative and discriminative learning; supervised, unsupervised, semi-supervised, and active learning; adaptive and multi-task learning; and Bayesian learning. These learning paradigms are motivated and discussed in the context of ASR technology and applications. We finally present and analyze recent developments of deep learning and learning with sparse representations, focusing on their direct relevance to advancing ASR technology.
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Keyword:
Li Deng
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.337.8867
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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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| 9 |
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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| 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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