Improved ASR for Under-Resourced Languages Through Multi-Task Learning with Acoustic Landmarks
Table 2: Decoding Error Rate for mono-phone (Mono) and tri-phone (Tri) on TIMIT and Iban.
['Corpus', 'AM', 'Baseline', 'MTL', 'MTL w/ Confid']
TIMIT results are reported to indicate the performance of Landmark-based MTL in the source language, prior to cross-language adaptation. The setups ”Iban-25%” and ”Iban-10%” train the AM on only 25% (100 minutes) and 10% (40 minutes) of the training data uniformly selected at random from the Iban training set (maintaining speaker and gender ratio), but evaluates the error rate on the full test set. As the amount of training data decreases, the benefits of MTL increase. When only 10% of training data is available, simulating a very low resource case, MTL reduces the word error rate by the greatest margin: 8.7% for monophone ASR and 6.17% for triphone ASR. Weighting the MTL loss according to confidence results in a small but consistent error rate reduction. All systems use the same language model, and all systems use acoustic models with the same network architecture and feature set; the error rate change we observe is caused entirely by the use of landmark-task MTL.