en-de
en-es
en-fr
en-sl
en
en-zh
0.25
0.5
0.75
1.25
1.5
1.75
2
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
Related categories
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