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Tandem Connectionist Feature Extraction for Conversational Speech Recognition

Published on Feb 25, 20075043 Views

Multi-Layer Perceptrons (MLPs) can be used in automatic speech recognition in many ways. A particular application of this tool over the last few years has been the Tandem approach, as described by Her

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Chapter list

Tandem Connectionist Feature Extraction for Conversational Speech Recognition00:01
Using Multi-Layer Perceptron (MLP) in Feature Extraction for Speech Recognition00:23
MLP outputs as features to HMM01:41
*1 Simple and Regular Within-Class Distribution03:04
Exp. 1: Posterior Feature Space04:09
Exp. 2: Log Posterior Feature Space04:55
Exp. 3: Typical Distributions of Log Posteriors in Histogram05:48
*2 Reducing Speaker Variation06:24
Exp. 4: Variances of (Speaker Adaptive Training) SAT Transforms for Different Speakers08:09
*3 Feature Combination: Better Performance, No Dimensionality Increase09:18
Usually What to Expect for a Feature Transform10:23
The Feature Generation Diagram11:06
Some Practical Details in Feature Generation and HMM Decoding12:30
Recognition Experiments13:31
Recognition with a ‘Plain’ System with ML Training14:30
Concerns for a Novel Feature: Scale and Carry Through15:29
Results with Adaptation16:04
Results in a Full-Fledged System16:49
Summary18:26