Agreement Learning for Automatic Accent Annotation

Automatic accent annotation is important in both speech synthesis and speech recognition. Existing statistical learning algorithms rely heavily on a sufficiently large set of labeled training samples that are expensive and time consuming to collect. For unlabeled data, unsupervised learning can be initiated with a small set of manually labeled data. This paper shows that the accuracy of automatic accent annotation can be improved by augmenting a small amount of manually labeled data with a large pool of unlabeled data. We introduce an agreement-learning algorithm for this propose. Experimental results show that it is possible to reduce human-labeling effort significantly while reducing up to 50% errors.