HMM Adaptation Using Linear Spline Interpolation with Integrated Spline Parameter Training for Robust Speech Recognition

We recently proposed a method for HMM adaptation to noisy

environments called Linear Spline Interpolation (LSI). LSI uses

linear spline regression to model the relationship between clean

and noisy speech features. In the original algorithm, stereo

training data was used to learn the spline parameters that min-

imize the error between the predicted and actual noisy speech

features. The estimated splines are then used at runtime to adapt

the clean HMMs to the current environment. While good results

can be obtained with this approach, the performance is limited

by the fact that the splines are trained independently from the

speech recognizer and as such, they may actually be subopti-

mal for adaptation. In this work, we introduce a new General-

ized EM algorithm for estimating the spline parameters using

the speech recognizer itself. Experiments on the Aurora 2 task

show that using LSI adaptation with splines trained in this man-

ner results in a 20% improvement over the original LSI algo-

rithm that used splines estimated from stereo data and a 28%

improvement over VTS adaptation.

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In  Interspeech

Publisher  International Speech Communication Association

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TypeInproceedings
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