Joint Modeling of Accents and Acousticsfor Multi-Accent Speech Recognition
Table 2: WERs of hard-switch using distorted AID. The rel. shows the relative improvement over ASpec; ind. AID applies an independent neural AID trained separately. Our Proposed Model applies the AID jointly learn with multi-accent AMs.
['Corpus', 'Pipelines with ind. AID ASpec', 'Pipelines with ind. AID MTLP (rel.)', 'Pipelines with ind. AID Joint (rel.)', 'Proposed Model (rel.)']
This section demonstrates the impact of imperfect AID on the performance using hard-switch. Given a well-trained independent AID (ind. AID), our joint model still significantly outperforms the two baseline models, and MTLP achieves better WER than ASpec. In comparison to oracle WERs of all models, British WERs are relatively constant without any distortion, however, American English WERs deteriorate accordingly. This is because independent AID has 100% recall for British English utterances on the test data.