Time and frequency filtering of filter-bank energies for robust HMM speech recognition
Every speech recognition system requires a signal representation that parametrically models the temporal evolution of the speech spectral envelope. Current parameterizations involve, either explicitly or implicitly, a set of energies from frequency bands which are often distributed in a mel scale. The computation of those energies is performed in diverse ways, but it always includes smoothing of basic spectral measurements and non-linear amplitude compression. Several linear transformations are then applied to the two-dimensional time-frequency sequence of energies before entering the HMM pattern matching stage. In this paper, a recently introduced technique that consists of filtering that sequence of energies along the frequency dimension is presented, and its resulting parameters are compared with the widely used cepstral coefficients. Then, that frequency filtering transformation is jointly considered with the time filtering transformation that is used to compute dynamic parameters, showing that the flexibility of this combined (tiffing) approach can be used to design a robust set of filters. Recognition experiment results are reported which show the potential of tiffing for an enhanced and more robust HMM speech recognition.
time and frequency filtering filter-bank energies robust speech recognition modulation spectrum