Keisuke Kinoshita

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Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. To evaluate state-of-the-art algorithms and obtain new insights regarding potential future research directions, we(More)
This paper proposes a statistical model-based speech dereverberation approach that can cancel the late reverberation of a reverberant speech signal captured by distant microphones without prior knowledge of the room impulse responses. With this approach, the generative model of the captured signal is composed of a source process, which is assumed to be a(More)
A speech signal captured by a distant microphone is generally smeared by reverberation, which severely degrades automatic speech recognition (ASR) performance. One way to solve this problem is to dereverberate the observed signal prior to ASR. In this paper, a room impulse response is assumed to consist of three parts: a direct-path response, early(More)
Speech recognition technology has left the research laboratory and is increasingly coming into practical use, enabling a wide spectrum of innovative and exciting voice-driven applications that are radically changing our way of accessing digital services and information. Most of today's applications still require a microphone located near the talker.(More)
In recent years, deep learning has not only permeated the computer vision and speech recognition research fields but also fields such as acoustic event detection (AED). One of the aims of AED is to detect and classify non-speech acoustic events occurring in conversation scenes including those produced by both humans and the objects that surround us. In AED,(More)
A speech signal captured by a distant microphone is generally smeared by reverberation, which severely degrades automatic speech recognition (ASR) performance. In this paper, we propose a novel dereverberation method utilizing multi-step forward linear prediction. It precisely estimates and suppresses the late reflections, which constitute a major cause of(More)
In this paper, we introduce a system for recognizing speech in the presence of multiple rapidly time-varying noise sources. The main components of the proposed approach are a modelbased speech enhancement pre-processor and an adaptation technique to optimize the integration between the pre-processor and the recognizer. The speech enhancement pre-processor(More)
BACKGROUND Whereas anterior cruciate ligament rupture usually requires reconstruction, the attachment between the tendon and the bone is the weakest region in the early posttransplantation period. In this process, the acquisition of appropriate vascularity is a key for early bone-tendon healing. HYPOTHESIS Granulocyte colony-stimulating factor has an(More)
It has recently been shown that the use of the time-varying nature of speech signals allows us to achieve high quality speech dereverberation based on multi-channel linear prediction (MCLP). However, this approach requires a huge computing cost for calculating large covariance matrices in the time domain. In addition, we face the important problem of how to(More)