In conventional paralinguistic classification approaches, information gained by low level features is described over broad segments (like whole turns) via statistical functionals. This procedure presumes meaningful information to be embodied within the whole segment. This assumption may be misleading if distinctive cues within a sample are surrounded by non-meaningful information or noise. In this case it would surely be beneficial to keep only parts of the sample that are most relevant for the recognition task. In this paper we propose a novel cluster-based approach, which aims at identifying frames likely to carry distinctive information. Evaluation is done within the INTERSPEECH 2012 Speaker Trait Challenge. Results show that under certain configurations frame pruning in fact leads to an improvement in recognition accuracy. On the observed corpus most stable improvements were achieved at a frame drop of 4-8%.

Index Terms: paralinguistic recognition, frame pruning, personality traits