Spoken-word recognition using dynamic features analysed by two-dimensional cepstrum

Abstract

In the paper, two-dimensional cepstrum (TDC) analysis and its application to word and monosyllable recognition are described. The TDC can simultaneously represent several different kinds of information contained in the speech waveform: static and dynamic features, as well as global and fine frequency structure. Noise reduction and speech enhancement can be easily performed using the TDC. Using word and monosyllable recognition experiments based on dynamic programming (DP) matching of a time sequence of the TDC, it is confirmed that the global static features (spectral envelope) and global dynamic features are both effective for speech recognition. A speaker-independent (noisy) word recognition algorithm is also proposed which recognises the words based on the similarity of dynamic features. The algorithm employs linear matching instead of DP nonlinear matching, requires a small amount of memory, and shows high speed and high accuracy in recognition. At present, the recognition rate is 89.0% at ∞ dB and 70.0% at 0 dB signal-to-noise ratio.