Noise-robust automatic speech recognition using a discriminative echo state network

The echo state network (ESN) is a recurrent neural network proposed by Herbert Jaeger with a simplified training routine. Previously, we have demonstrated the noise-robust performance of the predictive ESN classifier in automatic speech recognition experiments in which the network was trained to predict the next frame of speech features. Classification performance was limited because the predictive models lacked discriminability, so we changed the model output to a one-of-many output encoding scheme and trained the model discriminatively. Performance was compared to a hidden Markov model (HMM) in small-vocabulary ASR experiments with additive noise. Accuracy of 50% was achieved by a discriminative ESN classifier at -8.5 dB SNR, compared to 0.4 dB SNR for a predictive ESN classifier and 6.6 dB SNR for an HMM. With discriminative training, a larger reservoir was employed for the discriminative ESN classifier compared to the predictive ESN classifier which resulted a larger memory depth and more noise-robust performance.