Combining Acoustic Confidences and Pragmatic Plausibility for Classifying Spoken Chess Move Instructions
Malte Gabsdil · 2004
This paper describes a machine learning ap-proach to classifying n-best speech recogni-tion hypotheses as either correctly or incor-rectly recognised. The learners are trained on a combination of acoustic confidence features and move evaluation scores in a chess-playing scenario. The results show significant improve-ments over sharp baselines that use confidence rejection thresholds for classification. 1