Title:

Tandem connectionist feature stream extraction for conventional HMM systems

Author(s):

Hermansky, Hynek
Ellis, Daniel P. W.
Sharma, Sangita

Date:

2000

Type:

Articles

Department:

Electrical Engineering

Permanent URL:

http://hdl.handle.net/10022/AC:P:13821

Book/Journal Title:

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings, 5-9 June, 2000, Hilton Hotel and Convention Center, Istanbul, Turkey

Publisher:

IEEE

Publisher Location:

Piscataway, N.J.

Abstract:

Hidden Markov model speech recognition systems typically use Gaussian mixture models to estimate the distributions of decorrelated acoustic feature vectors that correspond to individual subword units. By contrast, hybrid connectionist-HMM systems use discriminatively-trained neural networks to estimate the probability distribution among subword units given the acoustic observations. In this work we show a large improvement in word recognition performance by combining neural-net discriminative feature processing with Gaussian-mixture distribution modeling. By training the network to generate the subword probability posteriors, then using transformations of these estimates as the base features for a conventionally-trained Gaussian-mixture based system, we achieve relative error rate reductions of 35% or more on the multicondition Aurora noisy continuous digits task

Subject(s):

Electrical engineering
Applied mathematics

DOI:

10.1109/ICASSP.2000.862024

Item views:

17

Metadata:

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