High‐resolution and efficient multiple‐string hypothesization using interword models

A new accurate string hypothesization algorithm to find multiple‐string hypotheses for speech recognition is proposed. The algorithm differs from the conventional N‐best search algorithms in that it allows the use of the same long‐term language (bigram) models and the same set of subword models, including the interword models, to perform both forward tree search and backward tree‐trellis stack decoding. The proposed A*‐based backward tree‐trellis stack decoding can handle interword units at the word boundaries, including the word boundaries of one‐phone words. Therefore, the interword context dependency is exactly preserved in both forward and backward multiple‐string hypotheses search. The search efficiency is maximized by applying the same high‐resolution acoustic and language models in both search directions. When the search heuristics are used, the proposed approach provides a more accurate string model matching than that of the conventional time‐synchronous beam search decoder. The proposed algorithm...