Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging

Autor:	Nguyen, Binh, Nguyen, Vu Bao Hung, Nguyen, Hien, Phuong, Pham Ngoc, Nguyen, The-Loc, Do, Quoc Truong, Mai, Luong Chi
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	In recent years, studies on automatic speech recognition (ASR) have shown outstanding results that reach human parity on short speech segments. However, there are still difficulties in standardizing the output of ASR such as capitalization and punctuation restoration for long-speech transcription. The problems obstruct readers to understand the ASR output semantically and also cause difficulties for natural language processing models such as NER, POS and semantic parsing. In this paper, we propose a method to restore the punctuation and capitalization for long-speech ASR transcription. The method is based on Transformer models and chunk merging that allows us to (1), build a single model that performs punctuation and capitalization in one go, and (2), perform decoding in parallel while improving the prediction accuracy. Experiments on British National Corpus showed that the proposed approach outperforms existing methods in both accuracy and decoding speed. Comment: 4 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1908.02404 Zobrazit plný text záznamu View this record from Arxiv