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20202022
most citedControllable neural text-to-speech synthesis using intuitive prosodic features

10 citations · 15 across the 5 of their papers we have counts for

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eess.AS20221 cited

Vocal effort modeling in neural TTS for improving the intelligibility of synthetic speech in noise

Tuomo Raitio, Petko Petkov, Jiangchuan Li +3

We present a neural text-to-speech (TTS) method that models natural vocal effort variation to improve the intelligibility of synthetic speech in the presence of noise. The method c…

eess.AS20212 cited

On-device neural speech synthesis

Sivanand Achanta, Albert Antony, Ladan Golipour +8

Recent advances in text-to-speech (TTS) synthesis, such as Tacotron and WaveRNN, have made it possible to construct a fully neural network based TTS system, by coupling the two com…

eess.AS20211 cited

Whispered and Lombard Neural Speech Synthesis

Qiong Hu, Tobias Bleisch, Petko Petkov +3

It is desirable for a text-to-speech system to take into account the environment where synthetic speech is presented, and provide appropriate context-dependent output to the user.…

eess.AS202010 cited

Controllable neural text-to-speech synthesis using intuitive prosodic features

Tuomo Raitio, Ramya Rasipuram, Dan Castellani

Modern neural text-to-speech (TTS) synthesis can generate speech that is indistinguishable from natural speech. However, the prosody of generated utterances often represents the av…

eess.AS20201 cited

Parametric Representation for Singing Voice Synthesis: a Comparative Evaluation

Onur Babacan, Thomas Drugman, Tuomo Raitio +2

Various parametric representations have been proposed to model the speech signal. While the performance of such vocoders is well-known in the context of speech processing, their ex…