LOW - RESOURCE NOISE - ROBUST FEATURE PO Chia - Ping

Noise-Robust Speech Features Based on Cepstral Time Coefficients

In this paper, we investigate the noise-robustness of features based on the cepstral time coefficients (CTC). By cepstral time coefficients, we mean the coefficients obtained from applying the discrete cosine transform to the commonly used mel-frequency cepstral coefficients (MFCC). Furthermore, we apply temporal filters used for computing delta and acceleration dynamic features to the CTC, res...

Towards Simple Methods of Noise Robustness

We introduce an effective and simple noise-robust feature processing technique which achieves very good results on the Aurora noisy-digits database. This technique does not require knowledge of the noise type and level. Also, it does not require any increase in modeling parameters. It performs well both on matched and mis-matched training and testing environments. In comparison to the Aurora ba...

Low-resource noise-robust feature post-processing on Aurora 2.0

We present a highly effective and extremely simple noiserobust front end based on novel post-processing of standard MFCC features. It performs remarkably well on the Aurora 2.0 noisydigits database without requiring any increase in model complexity. Compared to the Aurora 2.0 baseline system, our technique improves the average word error rate by 45% in the multicondition training case, (matched...

Empirical mode decomposition for noise-robust automatic speech recognition

In this paper, a novel technique based on the empirical mode decomposition (EMD) methodology is proposed and examined for the noise-robustness of automatic speech recognition systems. The EMD analysis is a generalization of the Fourier analysis for processing non-linear and non-stationary time functions, in our case, the speech feature sequences. We use the first and second intrinsic mode funct...

Using Teacher-Student Model For Emotional Speech Recognition[In Chinese]