A bag-of-features framework for incremental learning of speech invariants in unsegmented audio streams

International audienceWe introduce a computational framework that allows a machine to bootstrap flexible autonomous learning of speech recognition skills. Technically, this framework shall en- able a robot to incrementally learn to recog- nize speech invariants from unsegmented au- dio streams and with no prior knowledge of phonetics. To achieve this, we import the bag-of-words/bag-of-features approach from recent research in computer vision, and adapt it to incremental developmental speech pro- cessing. We evaluate an implementation of this framework on a complex speech database

Towards unsupervised online word clustering

Brandl H, Joublin F, Goerick C. Towards unsupervised online word clustering. In: IEEE International Conference on Acoustics, Speech, and Signal Processing. Las Vegas, NV; 2008: 5073-5076.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