CENSREC-3: Data Collection for In-Car Speech Recognition and Its Common Evaluation Framework

Author(s):  
M. Fujimoto ◽  
S. Nakamura ◽  
Toshiki Endo ◽  
K. Takeda ◽  
C. Miyajima ◽  
...  
2008 ◽  
Author(s):  
Kristie Nemeth ◽  
Nicole Arbuckle ◽  
Andrea Snead ◽  
Drew Bowers ◽  
Christopher Burneka ◽  
...  

Endoscopy ◽  
1992 ◽  
Vol 24 (S 2) ◽  
pp. 493-498 ◽  
Author(s):  
R. S. Johannes ◽  
D. L. Carr-Locke

2006 ◽  
Vol 14 (2) ◽  
pp. 411-442
Author(s):  
Dimitri Kanevsky ◽  
Sara Basson ◽  
Alexander Faisman ◽  
Leonid Rachevsky ◽  
Alex Zlatsin ◽  
...  

This paper outlines the background development of “intelligent” technologies such as speech recognition. Despite significant progress in the development of these technologies, they still fall short in many areas, and rapid advances in areas such as dictation are actually stalled. In this paper we have proposed semi-automatic solutions — smart integration of human and intelligent efforts. One such technique involves improvement to the speech recognition editing interface, thereby reducing the perception of errors to the viewer. Other techniques that are described in the paper are batch enrollment, which allows the user to reduce the amount of time required for enrollment, and content spotting, which can be used for applications that have repeated content flow, such as movies or museum tours. The paper also suggests a general concept of distributive training of speech recognition systems that is based on data collection across a network.


Author(s):  
Christine L Heidebrecht ◽  
Jeffrey C Kwong ◽  
Michael Finkelstein ◽  
Sherman D Quan ◽  
Jennifer A Pereira ◽  
...  

Author(s):  
Roger B. Garberg

Phoneme-based automatic speech recognition (ASR) technology enables designers to easily create custom command words or phrases that users can employ to request service operations. In this paper, I report results from two experiments concerning important dimensions of these ASR command vocabularies, including command naturalness/appropriateness and command recallability. Ease of recall is a critical dimension for assessing ASR commands used in multi-step applications since service subscribers may be engaged in several different cognitive activities that divide attention. Yet techniques for measuring command recallability can be difficult to implement owing to the time required for data collection and analysis. Results of these studies indicate the the dimension of command “naturalness” and memorability are closely related: under appropriate conditions, the simple procedures associated with measuring command naturalness or appropriateness can predict retrievability of command expressions.


2011 ◽  
Vol 32 (5) ◽  
pp. 201-210 ◽  
Author(s):  
Takahiro Fukumori ◽  
Takanobu Nishiura ◽  
Masato Nakayama ◽  
Yuki Denda ◽  
Norihide Kitaoka ◽  
...  

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