Author
Koichi Tsunoda
Bio: Koichi Tsunoda is an academic researcher from Nagoya University. The author has contributed to research in topics: Esophageal speech & Speech synthesis. The author has an hindex of 1, co-authored 1 publications receiving 63 citations.
Topics: Esophageal speech, Speech synthesis, Vowel, Laryngectomee
Papers
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TL;DR: A speech prosthesis has been developed based on the following idea: when a handicapped person such as a laryngectomee tries to speak in vain, the movements of the mouth, tongue, etc., are elicited and what he or she is trying to say can be determined.
Abstract: A speech prosthesis has been developed based on the following idea. When a handicapped person such as a laryngectomee tries to speak in vain, the movements of the mouth, tongue, etc., are elicited. By detecting the movements, what he or she is trying to say can be determined. Then a speech synthesizer is driven to produce a voice of good quality.
68 citations
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TL;DR: The article first outlines the emergence of the silent speech interface from the fields of speech production, automatic speech processing, speech pathology research, and telecommunications privacy issues, and then follows with a presentation of demonstrator systems based on seven different types of technologies.
436 citations
TL;DR: The major benefits and challenges of myoelectric interfaces are evaluated and recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers.
253 citations
20 Mar 2016
TL;DR: Lipreading, i.e. speech recognition from visual-only recordings of a speaker's face, can be achieved with a processing pipeline based solely on neural networks, yielding significantly better accuracy than conventional methods.
Abstract: Lipreading, i.e. speech recognition from visual-only recordings of a speaker's face, can be achieved with a processing pipeline based solely on neural networks, yielding significantly better accuracy than conventional methods. Feedforward and recurrent neural network layers (namely Long Short-Term Memory; LSTM) are stacked to form a single structure which is trained by back-propagating error gradients through all the layers. The performance of such a stacked network was experimentally evaluated and compared to a standard Support Vector Machine classifier using conventional computer vision features (Eigenlips and Histograms of Oriented Gradients). The evaluation was performed on data from 19 speakers of the publicly available GRID corpus. With 51 different words to classify, we report a best word accuracy on held-out evaluation speakers of 79.6% using the end-to-end neural network-based solution (11.6% improvement over the best feature-based solution evaluated).
184 citations
TL;DR: The new approach of phonetic feature bundling for modeling coarticulation in EMG-based speech recognition is described and results on theEMG-PIT corpus, a multiple speaker large vocabulary database of silent and audible EMG speech recordings, which was recently collected are reported.
161 citations
TL;DR: An overview of the various modalities, research approaches, and objectives for biosignal-based spoken communication is given.
Abstract: Speech is a complex process involving a wide range of biosignals, including but not limited to acoustics. These biosignals—stemming from the articulators, the articulator muscle activities, the neural pathways, and the brain itself—can be used to circumvent limitations of conventional speech processing in particular, and to gain insights into the process of speech production in general. Research on biosignal-based speech processing is a wide and very active field at the intersection of various disciplines, ranging from engineering, computer science, electronics and machine learning to medicine, neuroscience, physiology, and psychology. Consequently, a variety of methods and approaches have been used to investigate the common goal of creating biosignal-based speech processing devices for communication applications in everyday situations and for speech rehabilitation, as well as gaining a deeper understanding of spoken communication. This paper gives an overview of the various modalities, research approaches, and objectives for biosignal-based spoken communication.
150 citations