Pronunciation learning for named-entities through crowd-sourcing
Attapol Rutherford, Fuchun Peng, Françoise Beaufays · 2014
Obtaining good pronunciations for named-entities poses a challenge for automated speech recognition because named-entities are diverse in nature and origin, and new entities come up every day. In this paper, we investigate the feasibility of learning named-entity pronunciations using crowd-sourcing. By collecting audio samples from non-linguistic-expert speak-ers with Mechanical Turk and learning from them, we can quickly derive pronunciations that are more accurate in speech recognition tests than manual pronunciations generated by lin-guistic experts. Compared to traditional approaches of generat-ing pronunciations, this new approach proves to be cheap, fast, and quite accurate. 1.