PUB - Publikationen an der Universität Bielefeld

Understanding the bootstrapping process of speech representation in infants is one key issue towards systems which may provide human-like speech recognition abilities some day. Until now, almost all current speech recognition systems have failed to integrate learning into the recognition process. Here we propose a system for unsupervised word-clustering, which is able to recognize and learn the structure of speech online in a unified framework. To do so we’ve extended HMM-based filler-free keyword spotting with acoustic model acquisition. To evaluate and control the dynamics of the combined acquisition-recognition process we propose measures for model activity, model correlation and speech coverage.