Latent perceptual mapping with data-driven variable-length acoustic units for template-based speech recognition

In recent work, we introduced Latent Perceptual Mapping (LPM) [1], a new framework for acoustic modeling suitable for template-like speech recognition. The basic idea is to leverage a reduced dimensionality description of the observations to derive acoustic prototypes that are closely aligned with perceived acoustic events. Our initial work adopted a bag-of-frames strategy to represent relevant acoustic information within speech segments. In this paper, we extend this approach by better integrating temporal information into the LPM feature extraction. Specifically, we use variable-length units to represent acoustic events at the supra-frame level, in order to benefit from finer temporal alignments when deriving the acoustic prototypes. The outcome can be viewed as a generalization of both conventional template-based approaches and recently proposed sparse representation solutions. This extension is experimentally validated on a context-independent phoneme classification task using the TIMIT corpus.