Created by: kuke

The beam search decoder for deployment in PR#139 takes advantage of trie tree as the data structure for prefix search and finite-state transducers for spelling correction, which speedup the decoding process and lower the WER. With a larger (compared with the model in #115 ) well-trained acoustic model, parameters alpha and beta for the decoder are retuned on the development dataset of LibriSpeech, as shown in the figure below.

• alpha: language model weight
• beta: word insertion weight
• WER: word error rate

As usual, the WER is mainly affected by the variation of parameter alpha. And the optimal parameters pair appears at (alpha,beta) = (2.15, 0.35), which produces a minimum WER 7.87% on the test dataset of LibriSpeech, and attenuates the WER by 0.8% compared to the prototype decoder in Python.