A New Timit Benchmark for Context-Independent Phone Recognition Using Turbo Fusion

Abstract

In this work, we apply the recently proposed turbo fusion in conjunction with state-of-the-art convolutional neural networks as acoustic models to the standard phone recognition task on the TIMIT database. The turbo fusion operates on posterior streams stemming from standard filterbank features and from group delay (phase) features. By the iterative exchange of posterior information, the phone error rate is decreased down to 16.91% absolute, which is to our knowledge the best reported result on the TIMIT core test set so far using context-independent acoustic models, outperforming the previous respective benchmark by 4.4% relative.