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1
Unsupervised learning of spoken language with visual context
In: Neural Information Processing Systems (NIPS) (2019)
Abstract: Humans learn to speak before they can read or write, so why can't computers do the same? In this paper, we present a deep neural network model capable of rudimentary spoken language acquisition using untranscribed audio training data, whose only supervision comes in the form of contextually relevant visual images. We describe the collection of our data comprised of over 120,000 spoken audio captions for the Places image dataset and evaluate our model on an image search and annotation task. We also provide some visualizations which suggest that our model is learning to recognize meaningful words within the caption spectrograms.
URL: https://hdl.handle.net/1721.1/124455
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2
Learning Word-Like Units from Joint Audio-Visual Analysis ...
Harwath, David; Glass, James R.. - : arXiv, 2017
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3
Unsupervised Lexicon Discovery from Acoustic Input
In: Transactions of the Association for Computational Linguistics (2015)
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4
Learning lexicons from speech using a pronunciation mixture model
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 21 (2013) 2, 357-366
OLC Linguistik
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5
Combining missing-feature theory, speech enhancement, and speaker-dependent/-independent modeling for speech separation
In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 24 (2010) 1, 67-76
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6
Combining missing-feature theory, speech enhancement, and speaker-dependent/-independent modeling for speech separation
In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 24 (2010) 1, 67-76
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7
Speech rhythm guided syllable nuclei detection
In: IEEE (2009)
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8
On the phonetic information in ultrasonic microphone signals
In: IEEE (2009)
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9
Multistream Articulatory Feature-Based Models for Visual Speech Recognition
In: IEEE (2009)
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10
Research Developments and Directions in Speech Recognition and Understanding, Part 1
In: IEEE (2009)
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11
Unsupervised pattern discovery in speech
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 16 (2008) 1, 186-197
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OLC Linguistik
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12
An implementation of rational wavelets and filter design for phonetic classification
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 15 (2007) 3, 939-948
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13
Robust speaker recognition in noisy conditions
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 15 (2007) 5, 1711-1723
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14
Mathematical foundations of speech and language processing
Weber, Katrin (Mitarb.); Kedem, Benjamin (Mitarb.); Khudanpur, Sanjeev (Hrsg.). - New York [u.a.] : Springer, 2004
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15
A probabilistic framework for segment-based speech recognition
In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 17 (2003) 2, 137-152
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16
New computational paradigms for acoustic modeling in speech recognition
Russell, Martin J. (Hrsg.); Bilmes, Jeff A. (Hrsg.); Lefevre, Fabrice (Mitarb.)...
In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 17 (2003) 2-3, 107-305
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17
Finding Acoustic Regularities in Speech: Applications to Phonetic Recognition
In: DTIC AND NTIS (1988)
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18
Finding Acoustic Regularities in Speech: Applications to Phonetic Recognition.
In: DTIC AND NTIS (1988)
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19
Speech Communication
Stevens, Kenneth N.; Allen, Jonathan; Halle, Morris. - : Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT), 1987
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20
Speech Communication
Stevens, Kenneth N.; Allen, Jonathan; Halle, Morris. - : Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT), 1987
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