Browsing by Author "Kalwad, P.S."

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Automatic reflection removal using reflective layer image information
(Institute of Electrical and Electronics Engineers Inc., 2015) Prakash, D.; Kalwad, P.S.; Peddigari, V.; Srinivasa, P.
This paper tries to address the problem of removing unwanted reflection layer from the image mixtures. These reflections may occur due to semi-reflective glass mediums. This demands separating the input image into the reflecting layer and the subject layer, also known as, background layer which is the actual scene itself. But decomposing a single input image into these two layers is a massively ill-posed problem with infinite combinations of decomposition. This type of problem falls under the category of blind source separation. There are ample classical separation approaches available in the literature which either requires multiple images or works on a single image with user assistance. In this paper we propose a method for separating the two layers from a single input image without any user or human intervention using some prior information about the reflective layer. Â© 2015 IEEE.
Language modelling and english speech prediction system to aid people with stuttering disorder
(2015) Chandana, T.L.; Kalwad, P.S.; Pattanaik, S.; Ram Mohana Reddy, Guddeti
This paper proposes a novel method to predict the speech based on N-Gram language model for English Language. It also concentrates on how Speech Completion can be combined with stuttering detection to aid people suffering from this disorder to overcome psychological and social introversion. To the best of our knowledge, such systems exist only in Japanese language and hence, this paper is the first to introduce such an application for English language. The existing work in Japanese language uses a vocabulary tree structure for prediction in contrast to the n-gram language model used in this paper. The basic idea of the proposed work is to consider the user's speech input for detecting the repetition of words as stuttering. If this repetition of words is detected then, the next word can be predicted after eliminating the repeated word using the n-gram language model and the predicted word can be converted back to speech. Using this proposed methodology, we are able to achieve a prediction accuracy of 87% when a 10-fold test is carried out. � 2015 ACM.
Language modelling and english speech prediction system to aid people with stuttering disorder
(Association for Computing Machinery acmhelp@acm.org, 2015) Chandana, T.L.; Kalwad, P.S.; Pattanaik, S.; Guddeti, G.
This paper proposes a novel method to predict the speech based on N-Gram language model for English Language. It also concentrates on how Speech Completion can be combined with stuttering detection to aid people suffering from this disorder to overcome psychological and social introversion. To the best of our knowledge, such systems exist only in Japanese language and hence, this paper is the first to introduce such an application for English language. The existing work in Japanese language uses a vocabulary tree structure for prediction in contrast to the n-gram language model used in this paper. The basic idea of the proposed work is to consider the user's speech input for detecting the repetition of words as stuttering. If this repetition of words is detected then, the next word can be predicted after eliminating the repeated word using the n-gram language model and the predicted word can be converted back to speech. Using this proposed methodology, we are able to achieve a prediction accuracy of 87% when a 10-fold test is carried out. Â© 2015 ACM.