Automated L2 speech assessment applications need some mechanism for validating the relevance of user responses before providing scores. In this paper, we discuss a method for off-topic detection in an automated speech assessment application: a high-stakes English test (PTE Academic). Different from traditional topic detection techniques that use characteristics of text alone, our method mainly focused on using the features derived from speech confidence scores. We also enhanced our off-topic detection model by incorporating other features derived from acoustic likelihood, language model likelihood, and garbage modeling. The final combination model significantly outperformed classification from any individual feature. When fixing the false rejection rate at 5% in our test set, we achieved a false acceptance rate of 9.8%. a very promising result.
Bibliographic reference. Cheng, Jian / Shen, Jianqiang (2011): "Off-topic detection in automated speech assessment applications", In INTERSPEECH-2011, 1597-1600.