This work explores discriminative acoustic features: audio signal representations produced by multilayer perceptrons, such as Tandem and bottleneck features used in state-of-the-art automatic speech recognition systems. Experimental results highlight the factors that influence performance in terms of accuracy and speed; novel approaches are introduced to provide improvement in both regards. The overall emphasis is on discovering techniques that are suitable for practical deployment, translating effectiveness beyond a traditional research setting. Applications include real-time low-latency audio stream processing on mobile devices – as well as systems that are built with low-quality training data and must encounter the diverse complications of "real-world" use cases.
Title
Discriminative Acoustic Features for Deployable Speech Recognition
Published
2016-12-13
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2016-199
Type
Text
Extent
142 p
Archive
The Engineering Library
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