Improved recognition by combining different features and different systems
Daniel P. W. Ellis · 2000
Combining multiple estimators to obtain a more accurate final result is a well-known technique in statistics. In the domain of speech recognition, there are many ways in which this general principle can be applied. We have looked at several ways for combining the information from different feature representations, and used these results in the best-performing system in last year's Aurora evaluation: Our entry combined feature streams after the acoustic classification stage, then used a combination of neural networks and Gaussian mixtures for more accurate modeling. These and other approaches to combination are described and compared, and some more general questions arising from the combination of information streams are considered.