| Paper ID | SPE-10.6 |
| Paper Title |
HISTORY UTTERANCE EMBEDDING TRANSFORMER LM FOR SPEECH RECOGNITION |
| Authors |
Keqi Deng, Gaofeng Cheng, Haoran Miao, Pengyuan Zhang, Yonghong Yan, Institute of Acoustics, Chinese Academy of Sciences, China |
| Session | SPE-10: Speech Recognition 4: Transformer Models 2 |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-LVCR] Large Vocabulary Continuous Recognition/Search |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
History utterances contain rich contextual information; however, better extracting information from the history utterances and using it to improve the language model (LM) is still challenging. In this paper, we propose the history utterance embedding Transformer LM (HTLM), which includes an embedding generation network for extracting contextual information contained in the history utterances and a main Transformer LM for current prediction. In addition, the two-stage attention (TSA) is proposed to encode richer contextual information into the embedding of history utterances (h-emb) while supporting GPU parallel training. Furthermore, we combine the extracted h-emb and embedding of current utterance (c-emb) through the dot-product attention and a fusion method for HTLM's current prediction. Experiments are conducted on the HKUST dataset and achieve a 23.4% character error rate (CER) on the test set. Compared with the baseline, the proposed method yields 12.86 absolute perplexity reduction and 0.8% absolute CER reduction. |
