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Keywords
(6)
Articulatory Feature
Automatic Speech Recognition
Data Collection
Multilayer Perceptron
Speech Analysis
Speech Recognition
Related Publications
(1)
SVitchboard 1: small vocabulary tasks from Switchboard
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Manual Transcription of Conversational Speech at the Articulatory Feature Level
Manual Transcription of Conversational Speech at the Articulatory Feature Level,10.1109/ICASSP.2007.367229,Karen Livescu,Ari Bezman,M. Borges,Lisa Yun
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Manual Transcription of Conversational Speech at the Articulatory Feature Level
(
Citations: 3
)
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Karen Livescu
,
Ari Bezman
,
M. Borges
,
Lisa Yung
,
O. Cetin
,
J. Frankel
,
S. King
,
Xuemin Xhi
,
L. Lavoie
We present an approach for the manual labeling of speech at the
articulatory feature
level, and a new set of labeled conversational speech collected using this approach. A detailed transcription, including overlapping or reduced gestures, is useful for studying the great pronunciation variability in conversational speech. It also facilitates the testing of feature classifiers, such as those used in articulatory approaches to automatic speech recognition. We describe an effort to transcribe a small set of utterances drawn from the Switchboard database using eight articulatory tiers. Two transcribers have labeled these utterances in a multi-pass strategy, allowing for correction of errors. We describe the
data collection
methods and analyze the data to determine how quickly and reliably this type of transcription can be done. Finally, we demonstrate one use of the new data set by testing a set of
multilayer perceptron
feature classifiers against both the manual labels and forced alignments
Conference:
International Conference on Acoustics, Speech, and Signal Processing - ICASSP
, vol. 4, pp. IV-953-IV-956, 2007
DOI:
10.1109/ICASSP.2007.367229
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Citation Context
(3)
...For instance, [
20
] reported that transcriptions of utterances on the AF level take 1000 times real-time for SVitchboard [21] (a selection of the Switchboard corpus that can be considered a small vocabulary data set)...
...Therefore, in addition to evaluating the classifier on the TIMIT data, we also test the new acoustic features on those SVitchboard utterances that have been transcribed at the AF value level [
20
]...
...SVArticulatory was manually transcribed on the AF level for the 2006 JHU Summer Workshop [
20
]...
Barbara Schuppler
,
et al.
Using temporal information for improving articulatory-acoustic feature...
...The AF set here includes place and degree of constriction, nasality, rounding, glottal state, and vowel quality (see [
14
] for more details), and the classifiers are MLPs...
...for labeling is a slightly more detailed version of the observation modeling features; see [
14
] for more information...
Karen Livescu
,
et al.
Articulatory Feature-Based Methods for Acoustic and Audio-Visual Speec...
...Finally, an exploration of manual labelling of articulatory features and comparison with the classification made by the MLPs described in this paper can be found in [
9
]...
Joe Frankel
,
et al.
Articulatory feature classifiers trained on 2000 hours of telephone sp...
References
(7)
A Flexible Stream Architecture for ASR using Articulatory Features
(
Citations: 32
)
Florian Metze
,
Alex Waibel
Published in 2002.
A multichannel articulatory speech database and its application for automatic speech recognition
(
Citations: 36
)
Alan A. Wrench
,
William J. Hardcastle
Published in 2000.
INSIGHTS INTO SPOKEN LANGUAGE GLEANED FROM PHONETIC TRANSCRIPTION OF THE SWITCHBOARD CORPUS
(
Citations: 75
)
Steven Greenberg
,
Joy Hollenback
,
Dan Ellis
Published in 1996.
SVitchboard 1: small vocabulary tasks from Switchboard
(
Citations: 18
)
Simon King
,
Chris Bartels
,
Jeff Bilmesy
Conference:
Annual Conference of the International Speech Communication Association - INTERSPEECH
, pp. 3385-3388, 2005
Combining acoustic and articulatory feature information for robust speech recognition
(
Citations: 43
)
Katrin Kirchhoff
,
Gernot A. Fink
,
Gerhard Sagerer
Journal:
Speech Communication
, vol. 37, no. 3-4, pp. 303-319, 2002
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Citations
(3)
Using temporal information for improving articulatory-acoustic feature classification
Barbara Schuppler
,
J. J. H. C. van Doremalen
,
Odette Scharenborg
,
Bert Cranen
,
L. W. J. Boves
Journal:
Fundamenta Informaticae - FUIN
, 2009
Articulatory Feature-Based Methods for Acoustic and Audio-Visual Speech Recognition: Summary from the 2006 JHU Summer workshop
(
Citations: 26
)
Karen Livescu
,
O. Cetin
,
M. Hasegawa-Johnson
,
S. King
,
C. Bartels
,
N. Borges
,
A. Kantor
,
P. Lal
,
L. Yung
,
A. Bezman
,
S. Dawson-Haggerty
,
B. Woods
http://academic.research.microsoft.com/io.ashx?type=5&id=10061642&selfId1=0&selfId2=0&maxNumber=12&query=
Conference:
International Conference on Acoustics, Speech, and Signal Processing - ICASSP
, vol. 4, pp. IV-621-IV-624, 2007
Articulatory feature classifiers trained on 2000 hours of telephone speech
(
Citations: 13
)
Joe Frankel
,
Mathew Magimai-Doss
,
Simon King
,
Karen Livescu
,
Özgür Çetin
Conference:
International Conference on Acoustics, Speech, and Signal Processing - ICASSP
, pp. 2485-2488, 2007