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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18195

Title: Automatic Naturalness Recognition from Acted Speech Using Neural Networks
Authors: Atmaja, Bagus Tris
Sasou, Akira
Akagi, Masato
Keywords: speech naturalness recognition
acted dialogue
paralinguistic information
speech processing
speech analysis
Issue Date: 2021-12
Publisher: APSIPA
Magazine name: Proceedings, APSIPA Annual Summit and Conference 2021
Start page: 731
End page: 736
Abstract: This study proposes an automatic naturalness recognition from an acted dialogue. The problem can be stated that: given speech utterances with their naturalness labels, is it possible to recognize these labels automatically? By what methods? And how to evaluate these methods? We evaluated two supervised classifiers to investigate the possibility of recognizing naturalness automatically in acted speech: long short-term memory and multilayer perceptron neural networks. These classifiers accept inputs in the form of acoustic features from a speech dataset. Two kinds of acoustic features were evaluated: low-level and high-level features. This initial study on automatic naturalness recognition of speech resulted in a moderate performance of the assessed systems. We measured the performance in concordance correlation coefficients, Pearson correlation coefficients, and root mean square errors. This study opens a potential application of speech processing techniques for measuring naturalness in acted dialogue, which benefits for drama- or movie-making in the future.
Rights: Copyright (C) 2021 APSIPA. This material is posted here with permission of APSIPA (Asia-Pacific Signal and Information Processing Association). Bagus Tris Atmaja Akira Sasou and Masato Akagi, Proceedings of APSIPA Annual Summit and Conference 2021
URI: http://hdl.handle.net/10119/18195
Material Type: publisher
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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