Word Level LM at Line 96

[kmohanku]

I have a KenLM scoring integrated at Line 96. The performance on my test set (Both LM and Test set are LibriSpeech based) is worse than not using an LM at all. I am scoring only at space, multiplying the log probability (Converted from log10) by Alpha and also compensating with bonus term by adding (beta * log(word count in prefix)). I am applying this only to "not blank" probability. I have no success. Has anyone achieved success by integrating LM scoring?

I used my test set and Language model with Paddle Paddle decoder with same acoustic model and there was a 6% improvement in WER. They have a trie based LM aided by WFST correction along with this beam search algo. I would appreciate any pointers or help here. Thanks!