N‐best breadth search for large vocabulary continuous speech recognition using a long span language model

In a large vocabulary continuous speech recognition system, high‐level linguistic knowledge can enhance performance. However, integration of high‐level linguistic knowledge and complex acoustic models under an efficient search scheme is still problematic. Higher‐order n‐grams are so computationally expensive, especially when the size of vocabulary is large, that real time processing is not possible yet. In this report, the n‐best breadth search algorithm is proposed under the framework of the state space search, which can handle higher order n‐grams and complex subword acoustic models such as the cross‐word triphones. The n‐best breadth search is a combination of the best first search and the breadth first search. The proposed algorithm can be extended to handle other types of language models such as the stochastic context‐free grammar, and different types of acoustic models including the neural networks. Compared with the conventional beam‐search method, this pilot experiment shows that the proposed algo...