A nativeness classifier for TED Talks

This paper presents a nativeness classifier for English. The detector was developed and tested with TED Talks collected from the web, where the major non-native cues are in terms of segmental aspects and prosody. The first experiments were made using only acoustic features, with Gaussian supervectors for training a classifier based on support vector machines. These experiments resulted in an equal error rate of 13.11%. The following experiments based on prosodic features alone did not yield good results. However, a fused system, combining acoustic and prosodic cues, achieved an equal error rate of 10.58%. A small human benchmark was conducted, showing an inter-rater agreement of 0.88. This value is also very close to the agreement value between humans and the best fused system.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Jose Lopes, Isabel Trancoso, Alberto Abad
• Keywords: Equal Error Rate, ICASSP 2011, Major Non-native Cues, Signal Processing, Support Vector Machines