Acoustic Look-Ahead for More Efficient Decoding in LVCSR

摘要

In this paper we propose novel approximations of a generalized acoustic look-ahead to speed up the search process in large vocabulary continuous speech recognition (LVCSR). Unlike earlier methods, we do not employ any phoneme- or syllable level heuristics. First we define and analyze the perfect acoustic look-ahead as a simple pre-evaluation of the original acoustic models into the future. This method is very slow, but reveals the best possible impact on the search space that can be achieved through acoustic look-ahead. In a second step, we derive efficient and simple approximative look-ahead models from the perfect models. We show that the approximative models compare well to the perfect models regarding the search space, and that the approximative models significantly improve the efficiency in comparison to the baseline, without any negative effect on the precision.