These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Large-Scale End-to-End Multilingual Speech Recognition and Language Identification with Multi-Task Learning
In this paper, we report a large-scale end-to-end language-independent multilingual model for joint automatic speech recognition (ASR) and language identification (LID). This model adopts hybrid CTC/attention architecture and achieves word error rate (WER) of 52.8 and LID accuracy of 93.5 on 42 languages with around 5000 hours of training data. We also compare the effects of using subword-level or character-level vocabulary for large-scale multilingual tasks. Furthermore, we transfer the pre-trained model to 14 low-resource languages. Results show that the pre-trained model achieves significantly better results than non-pretrained baselines on both language specific and multilingual low-resource ASR tasks in terms of WER, which is reduced by 28.1% and 11.4% respectively.
About the metric use case
You can click on the links to see the associated metric use cases
Purpose(s):
