Chiyomi Miyajima

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| All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following(More)
This paper presents a framework for designing a hidden Markov model (HMM)-based audio-visual automatic speech recognition (ASR) system based on minimum classification error training. Audio/visual HMM parameters are optimized with the generalized probabilistic descent (GPD) method, and their likelihoods are combined using model-dependent stream weights which(More)
This paper introduces an evaluation framework for Japanese noisy speech recognition named AURORA-2J. Speech recognition systems must still be improved to be robust to noisy environments, but this improvement requires development of the standard evaluation corpus and assessment technologies. Recently, the Aurora 2, 3 and 4 corpora and their evaluation(More)
This paper describes an approach for feature extraction in speech recognition systems using kernel principal component analysis (KPCA). This approach consists in representing speech features as the projection of the extracted speech features mapped into a feature space via a nonlinear mapping onto the principal components. The nonlinear mapping is(More)
This paper investigates the effectiveness of the DAEM (Deterministic Annealing EM) algorithm in acoustic modeling for speaker and speech recognition. Although the EM algorithm has been widely used to approximate the ML estimates, it has the problem of initialization dependence. To relax this problem, the DAEM algorithm has been proposed and confirmed the(More)
Voice activity detection (VAD) plays an important role in speech processing including speech recognition, speech enhancement, and speech coding in noisy environments. We developed an evaluation framework for VAD in such environments, called Corpus and Environment for Noisy Speech Recognition 1 Concatenated (CENSREC1-C). This framework consists of noisy(More)
In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, are measured using a driving simulator. We compared(More)
This paper considers a comprehensive and collaborative project to collect large amounts of driving data on the road for use in a wide range of areas of vehicle-related research centered on driving behavior. Unlike previous data collection efforts, the corpora collected here contain both human and vehicle sensor data, together with rich and continuous(More)
This paper presents a new approach to modeling speech spectra and pitch for text-independent speaker identification using Gaussian mixture models based on multi-space probability distribution (MSD-GMM). The MSD-GMM allows us to model continuous pitch values for voiced frames and discrete symbols representing unvoiced frames in a unified framework. Spectral(More)
This paper presents a virtual push button interface created by drawing a shape or line in the air with a fingertip. As an example of such a gesture-based interface, we developed a four-button interface for entering multi-digit numbers by pushing gestures within an invisible 2x2 button matrix inside a square drawn by the user. Trajectories of fingertip(More)