Name
Abstract | ||
|---|---|---|
Recent advances in deep learning based large vocabulary con- tinuous speech recognition (LVCSR) invoke growing demands in large scale speech transcription. The inference process of a speech recognizer is to find a sequence of labels whose corresponding acoustic and language models best match the input feature [1]. The main computation includes two stages: acoustic model (AM) inference and linguistic search (weighted finite-state transducer, WFST). Large computational overheads of both stages hamper the wide application of LVCSR. Benefit from stronger classifiers, deep learning, and more powerful computing devices, we propose general ideas and some initial trials to solve these fundamental problems. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computation and Language | Speech transcription,Inference,Computer science,Natural language processing,Artificial intelligence,Deep learning,Linguistics,Vocabulary,Language model,Computation,Acoustic model |
DocType | Volume | Citations |
Journal | abs/1808.00687 | 0 |
PageRank | References | Authors |
0.34 | 0 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhehuai Chen | 1 | 11 | 3.89 |