Papers by Hiroshi Saruwatari
Personalized Filled-pause Generation with Group-wise Prediction Models (2022.lrec-1)
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| Challenge: | Disfluency generation is a method to generate personalized filled pauses (FPs) compared with fluent text generation, it is difficult to predict them because of the sparsity of position and frequency difference between more and less frequently used FPs. |
| Approach: | They propose a method to generate personalized filled pauses (FPs) by group-wise prediction models. |
| Outcome: | The proposed method generates personalized filled pauses (FPs) with group-wise prediction models. |
CPJD Corpus: Crowdsourced Parallel Speech Corpus of Japanese Dialects (L18-1)
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| Challenge: | Various corpora of dialects have been collected using a well-equipped recording environment due to geographical and expense issues. |
| Approach: | They construct a crowdsourced parallel speech corpus of Japanese dialects using crowdsourcing platforms. |
| Outcome: | The proposed corpus includes parallel text and speech data of 21 Japanese dialects. |
SMASH Corpus: A Spontaneous Speech Corpus Recording Third-person Audio Commentaries on Gameplay (2020.lrec-1)
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| Challenge: | Developing a spontaneous speech corpus is important for spoken language research . a corpus of spontaneous speech is needed to develop these techniques . |
| Approach: | They propose to use Japanese male commentators' spontaneous speech to construct a SMASH corpus . they use transcriptions and topic tags to annotate the commentaries and report some results . |
| Outcome: | The proposed corpus includes spontaneous speech of two Japanese male commentators . the authors report that the annotations yielded a better corpus than the previous methods . |
DNN-based Speech Synthesis Using Abundant Tags of Spontaneous Speech Corpus (2020.lrec-1)
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Yuki Yamashita, Tomoki Koriyama, Yuki Saito, Shinnosuke Takamichi, Yusuke Ijima, Ryo Masumura, Hiroshi Saruwatari
| Challenge: | Experimental evaluation results show that rich annotations enhance the reproducibility of paralinguistic features of synthetic speech. |
| Approach: | They investigate the effectiveness of using rich annotations in deep neural network-based statistical speech synthesis. |
| Outcome: | The proposed method improves reproducibility of paralinguistic features of synthetic speech . the corpus of spontaneous Japanese (CSJ) has large annotations on paralinguistic and nonlinguistic features . |