Paper Info
Title
The HW-TSC's Speech to Speech Translation System for IWSLT 2022 Evaluation.
Abstract
The paper presents the HW-TSC’s pipeline and results of Offline Speech to Speech Translation for IWSLT 2022. We design a cascade system consisted of an ASR model, machine translation model and TTS model to convert the speech from one language into another language(en-de). For the ASR part, we find that better performance can be obtained by ensembling multiple heterogeneous ASR models and performing reranking on beam candidates. And we find that the combination of context-aware reranking strategy and MT model fine-tuned on the in-domain dataset is helpful to improve the performance. Because it can mitigate the problem that the inconsistency in transcripts caused by the lack of context. Finally, we use VITS model provided officially to reproduce audio files from the translation hypothesis.
Year
DOI
Venue
2022
10.18653/v1/2022.iwslt-1.26
International Conference on Spoken Language Translation (IWSLT)
DocType
Volume
Citations 
Conference
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)
0
PageRank 
References 
Authors
0.34
0
12
Name
Order
Citations
PageRank
Jiaxin Guo101.69
Yinglu Li201.01
Minghan Wang305.07
Xiaosong Qiao401.01
Yuxia Wang502.70
Hengchao Shang604.39
Chang Su703.38
Yimeng Chen804.06
Min Zhang91849157.00
Shimin Tao1004.73
Hao Yang1107.44
Ying Qin1205.75