Towards unsupervised online word clustering

Understanding the bootstrapping process of speech representation in infants is one key issue towards systems which may provide humanlike 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.