Distinctive phonetic feature extraction for robust speech recognition

The paper describes an attempt to extract distinctive phonetic features (DPFs) that represent articulatory gestures in linguistic theory by using a multilayer neural network (MLN) and to apply the DPFs to noise-robust speech recognition. In the DPF extraction stage, after converting a speech signal to acoustic features composed of local features (LFs), an MLN with 33 output units, corresponding to context-dependent DPFs of 11 DPFs, 11 preceding context DPFs, and 11 following context DPFs, maps the LFs to DPFs. The proposed DPF parameters without MFCC (Mel-frequency cepstral coefficients) were firstly evaluated in comparison with a standard parameter set of MFCC and dynamic features on a word recognition task using clean speech; the result showed the same performance as that of the standard set. Noise robustness of these parameters was then tested with four types of additive noise and the proposed DPF parameters outperformed the standard set except for one additive noise type.