A unified framework for filterbank and time-frequency basis vectors in ASR frontends

For many years, filterbank have been widely used as one step of frontend feature extraction for Automatic Speech Recognition (ASR). In this paper, we propose a unified framework for ASR frontends, by first moving the nonlinear amplitude scaling, and then combining the filterbank weights with the cosine basis vectors. As part of this framework, we also show that the delta terms used to encode feature dynamics can also be viewed as one realization of a set of “unified” basis vectors over time. With this framework, frontends can be developed, analyzed and evaluated through their basis vectors over frequency and time.