Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals

Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. In this paper, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire language and acoustic models from observed continuous speech signals. For this purpose, we propose an integrative generative model that combines a language model and an acoustic model into a single generative model called the "hierarchical Dirichlet process hidden language model" (HDP-HLM). The HDP-HLM is obtained by extending the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by Johnson et al. An inference procedure for the HDP-HLM is derived using the blocked Gibbs sampler originally proposed for the HDP-HSMM. This procedure enables the simultaneous and direct inference of language and acoustic models from continuous speech signals. Based on the HDP-HLM and its inference procedure, we developed a novel double articulation analyzer. By assuming HDP-HLM as a generative model of observed time series data, and by inferring latent variables of the model, the method can analyze latent double articulation structure, i.e., hierarchically organized latent words and phonemes, of the data in an unsupervised manner. The novel unsupervised double articulation analyzer is called NPB-DAA. The NPB-DAA can automatically estimate double articulation structure embedded in speech signals. We also carried out two evaluation experiments using synthetic data and actual human continuous speech signals representing Japanese vowel sequences. In the word acquisition and phoneme categorization tasks, the NPB-DAA outperformed a conventional double articulation analyzer (DAA) and baseline automatic speech recognition system whose acoustic model was trained in a supervised manner.Comment: 15 pages, 7 figures, Draft submitted to IEEE Transactions on Autonomous Mental Development (TAMD

Human-vehicle collaborative driving to improve transportation safety

This dissertation proposes a collaborative driving framework which is based on the assessments of both internal and external risks involved in vehicle driving. The internal risk analysis includes driver drowsiness detection, driver distraction detection, and driver intention recognition which help us better understand the human driver's behavior. Steering wheel data and facial expression are used to detect the drowsiness. Images from a camera observing the driver are used to detect various types of driver distraction by using the deep learning approach. Hidden Markov Models (HMM) is implemented to recognize the driver's intention using the vehicle's laneposition, control and state data. For the external risk analysis, the co-pilot utilizes a Collision Avoidance System (CAS) to estimate the collision probability between the ego vehicle and other vehicles. Based on these two risk analyses, a novel collaborative driving scheme is proposed by fusing the control inputs from the human driver and the co-pilot to obtain the final control input for the vehicle under different circumstances. The proposed collaborative driving framework is validated in an Intelligent Transportation System (ITS) testbed which enables both autonomous and manual driving capabilities