Data Augmentation for Pipeline-Based Speech Translation
By Diego Alves, Askars Salimbajevs, Marcis Pinnis. 2020
5 citations recorded by the source corpus.
Pipeline-based speech translation methods may suffer from errors found in speech recognition system output. Therefore, it is crucial that machine translation systems are trained to be robust against such noise. In this paper, we propose two methods for parallel data augmentation for pipeline-based speech translation system development. The first method utilises a speech processing workflow to introduce errors and the second method generates commonly found suffix errors using a rule-based method. We show that the methods in combination allow significantly improving speech translation quality by 1.87 BLEU points over a baseline system.
Subfields: Artificial Intelligence, Hardware and Architecture, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Varun Prashant Gangal
- Soroush Vosoughi
- Eduard H. Hovy
- Teruko Mitamura
- Jason Wei
- Steven Y. Feng
- Sarath Chandar
- Mohit Bansal
In Natural Language Processing
- Varun Prashant Gangal
- Eduard H. Hovy
- Teruko Mitamura
- Soroush Vosoughi
- Jason Wei
- Steven Y. Feng
- Sarath Chandar
- Mohit Bansal
Citations in the corpus
- A Survey of Data Augmentation Approaches for NLP (2021, methodology): “Alves et al. (2020) Propose two methods for pipeline-based speech translation through the introduction of errors through 1. utilizing a speech processing workflow and 2. a rule-based method.”