Speech recognition using cepstral articulatory features

Though speech recognition has been widely investigated in the past decades, the role of articulation in recognition has received scant attention. Recognition accuracy increases when recognizers are trained with acoustic features in conjunction with articulatory ones. Traditionally, acoustic features are represented by mel-frequency cepstral coefficients (MFCCs) while articulatory features are represented by the locations or trajectories of the articulators. We propose the articulatory cepstral coefficients (ACCs) as features which are the cepstral coefficients of the time-location articulatory signal. We show that ACCs yield state-of-the-art results in phoneme classification and recognition on benchmark datasets over a wide range of experiments. The similarity of MFCCs and ACCs and their superior performance in isolation and conjunction indicate that common algorithms can be effectively used for acoustic and articulatory signals.