TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Human Laughter Generation using Hybrid Generative Models |
| 영문제목(English Title) |
Human Laughter Generation using Hybrid Generative Models |
| 저자(Author) |
Nadia Mansouri
Zied Lachiri
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| 원문수록처(Citation) |
VOL 15 NO. 05 PP. 1590 ~ 1609 (2021. 05) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Laughter is one of the most important nonverbal sound that human generates. It is a means for expressing his emotions. The acoustic and contextual features of this specific sound are different from those of speech and many difficulties arise during their modeling process. During this work, we propose an audio laughter generation system based on unsupervised generative models: the autoencoder (AE) and its variants. This procedure is the association of three main sub-process, (1) the analysis which consist of extracting the log magnitude spectrogram from the laughter database, (2) the generative models training, (3) the synthesis stage which incorporate the involvement of an intermediate mechanism: the vocoder. To improve the synthesis quality, we suggest two hybrid models (LSTM-VAE, GRU-VAE and CNN-VAE) that combine the representation learning capacity of variational autoencoder (VAE) with the temporal modelling ability of a long short-term memory RNN (LSTM) and the CNN ability to learn invariant features. To figure out the performance of our proposed audio laughter generation process, objective evaluation (RMSE) and a perceptual audio quality test (listening test) were conducted. According to these evaluation metrics, we can show that the GRU-VAE outperforms the other VAE models.
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| 키워드(Keyword) |
Laughter Synthesis
Variational Autoencoder
Autoencoder
Objective
Subjective Evaluation
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| 파일첨부 |
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