A Deliberation-based Joint Acoustic and Text Decoder

Autor:	Mavandadi, Sepand, Sainath, Tara N., Hu, Ke, Wu, Zelin
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Computation and Language Computer Science - Machine Learning Computer Science - Sound
Druh dokumentu:	Working Paper
Popis:	We propose a new two-pass E2E speech recognition model that improves ASR performance by training on a combination of paired data and unpaired text data. Previously, the joint acoustic and text decoder (JATD) has shown promising results through the use of text data during model training and the recently introduced deliberation architecture has reduced recognition errors by leveraging first-pass decoding results. Our method, dubbed Deliberation-JATD, combines the spelling correcting abilities of deliberation with JATD's use of unpaired text data to further improve performance. The proposed model produces substantial gains across multiple test sets, especially those focused on rare words, where it reduces word error rate (WER) by between 12% and 22.5% relative. This is done without increasing model size or requiring multi-stage training, making Deliberation-JATD an efficient candidate for on-device applications. Comment: Interspeech 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.15293 Zobrazit plný text záznamu View this record from Arxiv