Affine invariant features and their application to speech recognition

This paper proposes a set of affine invariant features (AIFs) for sequence data. The proposed AIFs can be calculated directly from the sequence data, and their invariance to affine transformation is proved mathematically through algebraic calculation. We apply the AIFs to speech recognition. Since the vocal tract length (VTL) difference causes to frequency warping which can be approximated well by affine transform on cepstral features, the AIFs of cepstral sequence provide robust features for VTL variations. We experimentally examine the invariance of AIFs of speech signals, and apply AIFs for Japanese isolated word recognition. The experimental results show that the combination of AIFs with MFCC or MFCC+Delta can lead to higher recognition rates than MFCC or MFCC+Delta only. Especially in the mismatched experiments, the combination with AIFs can reduce the error rates about 30% when compared to MFCC or MFCC+Delta only. The AIFs are expected to have other applications than speech recognition, since their invariance is general.