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This paper presents a statistical approach to synthesizing emphasized speech based on hidden Markov models (HMMs). Context-dependent HMMs are trained using emphasized speech data uttered by intentionally emphasizing an arbitrary accentual phrase in a sentence. To model acoustic characteristics of emphasized speech, new contextual factors describing an emphasized accentual phrase are additionally considered...
The impact of extensively usage visual display terminal (VDT) on automatic nervous system are observed and analyzed based on physiological information, i.e., electroencephalogram (EEG), heart rate variability (HRV) and thermograph. Two types of experiment which have different intermittent schedule were conducted using the Kraepelin psycho diagnostic test. The purpose is to determine the given task...
In the paper, we present a new fast and memory efficient VLSI architecture for output probability computations of continuous hidden Markov models (HMMs). The computations are the most time-consuming part of HMM-based recognition systems. High-speed VLSI architectures for the computations with small register size and low-power dissipation are required for the development of mobile embedded systems...
This paper proposes an acoustic modeling technique based on an additive structure of context dependencies for HMM-based speech recognition. Typical context dependent models, e.g., triphone HMMs, have direct dependencies of phonetic contexts, i.e., if a phonetic context is given, the Gaussian distribution is specified immediately. This paper assumes a more complex structure, an additive structure of...
This paper describes a method for determining the vocal tract spectrum from articulatory movements using an hidden Markov models (HMMs). In the proposed system, articulatory parameters are generated from a TTS system and converted to acoustic features to be synthesized. Comparing with conventional GMM-based systems, the proposed system has two additional properties: 1) phonetic information given input...
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