Abstract
This paper explores how different synthetic speech systems can be understood in a noisy environment that resembles radio noise. This work is motivated by a need for intelligible speech in noisy environments such as emergency response and disaster notification. We discuss prior work done on listening tasks as well as speech in noise. We analyze three different speech synthesizers in three different noise settings. We measure quantitatively the intelligibility of each synthesizer in each noise setting based on human performance on a listening task. Finally, treating the synthesizer and its generated audio as a black box, we present how word level and sentence level input choices can lead to increased or decreased listener error rates for synthesized speech.
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Acknowledgments
We would like to acknowledge several people for their help and support on this work. Particularly Carolyn Penstein, Rajat Kulshreshtha, Abhilasha Ravichander, and the officers of CMU EMS. As well as the several people who helped edit this work, especially Elise Romberger. Finally, thank you to reviewers reading and examining our experiments, methodology, and submission.
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Manzini, T., Black, A. (2018). Towards Improving Intelligibility of Black-Box Speech Synthesizers in Noise. In: Karpov, A., Jokisch, O., Potapova, R. (eds) Speech and Computer. SPECOM 2018. Lecture Notes in Computer Science(), vol 11096. Springer, Cham. https://doi.org/10.1007/978-3-319-99579-3_39
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DOI: https://doi.org/10.1007/978-3-319-99579-3_39
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