Junichi Yamagishi


2020

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Viable Threat on News Reading: Generating Biased News Using Natural Language Models
Saurabh Gupta | Hong Huy Nguyen | Junichi Yamagishi | Isao Echizen
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

Recent advancements in natural language generation has raised serious concerns. High-performance language models are widely used for language generation tasks because they are able to produce fluent and meaningful sentences. These models are already being used to create fake news. They can also be exploited to generate biased news, which can then be used to attack news aggregators to change their reader’s behavior and influence their bias. In this paper, we use a threat model to demonstrate that the publicly available language models can reliably generate biased news content based on an input original news. We also show that a large number of high-quality biased news articles can be generated using controllable text generation. A subjective evaluation with 80 participants demonstrated that the generated biased news is generally fluent, and a bias evaluation with 24 participants demonstrated that the bias (left or right) is usually evident in the generated articles and can be easily identified.

2018

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Identifying Computer-Translated Paragraphs using Coherence Features
Hoang-Quoc Nguyen-Son | Huy H. Nguyen | Ngoc-Dung T. Tieu | Junichi Yamagishi | Isao Echizen
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

2016

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Continuous Expressive Speaking Styles Synthesis based on CVSM and MR-HMM
Jaime Lorenzo-Trueba | Roberto Barra-Chicote | Ascension Gallardo-Antolin | Junichi Yamagishi | Juan M. Montero
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper introduces a continuous system capable of automatically producing the most adequate speaking style to synthesize a desired target text. This is done thanks to a joint modeling of the acoustic and lexical parameters of the speaker models by adapting the CVSM projection of the training texts using MR-HMM techniques. As such, we consider that as long as sufficient variety in the training data is available, we should be able to model a continuous lexical space into a continuous acoustic space. The proposed continuous automatic text to speech system was evaluated by means of a perceptual evaluation in order to compare them with traditional approaches to the task. The system proved to be capable of conveying the correct expressiveness (average adequacy of 3.6) with an expressive strength comparable to oracle traditional expressive speech synthesis (average of 3.6) although with a drop in speech quality mainly due to the semi-continuous nature of the data (average quality of 2.9). This means that the proposed system is capable of improving traditional neutral systems without requiring any additional user interaction.

2015

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A Comparison of Manual and Automatic Voice Repair for Individual with Vocal Disabilities
Christophe Veaux | Junichi Yamagishi | Simon King
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

2013

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Towards Personalised Synthesised Voices for Individuals with Vocal Disabilities: Voice Banking and Reconstruction
Christophe Veaux | Junichi Yamagishi | Simon King
Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies