Advanced search space pruning with acoustic look-ahead for WFST based LVCSR

In this work we show how some concepts already known from dynamic network decoding can be used to improve the efficiency of WFST based decoders. First we apply the concept of acoustic look-ahead to a WFST based decoder, and then we analyze the applicability of LM state pruning, a well motivated pruning method which is fundamental to token-passing decoders. The structure of the composed WFST search network makes it difficult to motivate advanced pruning methods, and consequently it is difficult to achieve a real reduction in search space. Nonetheless, we show how LM state pruning can be applied to WFST based decoders to improve their efficiency. The search space can be reduced by up to 50% at equal precision through acoustic look-ahead. Since our decoder follows a dynamic composition approach, the advantage in search space does not fully transfer to the RTF, which can be reduced by around 20% through acoustic look-ahead, and additional 5% through LM state pruning.