├── wavs
├── 5K_jeong_5.wav
├── 5K_kang_5.wav
├── 5K_yoon_5.wav
├── D12-250_4.wav
├── D12-250_5.wav
├── D12-500_4.wav
├── D12-500_5.wav
├── D13-250_4.wav
├── D13-250_5.wav
├── D13-500_4.wav
├── D13-500_5.wav
├── D14-250_4.wav
├── D14-250_5.wav
├── D14-500_4.wav
├── D14-500_5.wav
├── judy-250_2.wav
├── judy-250_3.wav
├── judy-250_5.wav
├── judy-500_2.wav
├── judy-500_3.wav
├── judy-500_5.wav
├── judy_5K_3.wav
├── kang_250_4.wav
├── kang_250_5.wav
├── kang_500_4.wav
├── kang_500_5.wav
├── mary-250_2.wav
├── mary-250_3.wav
├── mary-250_5.wav
├── mary-500_2.wav
├── mary-500_3.wav
├── mary-500_5.wav
├── mary_5K_3.wav
├── yoon_250_4.wav
├── yoon_250_5.wav
├── yoon_500_4.wav
├── yoon_500_5.wav
├── D12-500-5k_4.wav
├── D12-500-5k_5.wav
├── D13-500-5k_4.wav
├── D13-500-5k_5.wav
├── D14-500-5k_4.wav
├── D14-500-5k_5.wav
├── jeong_250_4.wav
├── jeong_250_5.wav
├── jeong_500_4.wav
├── jeong_500_5.wav
├── miller-250_2.wav
├── miller-250_3.wav
├── miller-250_5.wav
├── miller-500_2.wav
├── miller-500_3.wav
├── miller-500_5.wav
└── miller_5K_3.wav
├── styles.css
└── index.html
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/styles.css:
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1 | .heading {
2 | margin-top: 40px;
3 | font-weight: 900;
4 | }
5 | span {
6 | font-weight: 900;
7 | }
8 | .audio-list {
9 | display: flex;
10 | /* justify-content: space-between; */
11 | }
12 | .audio-element {
13 | margin-right: 10px;
14 | }
15 | h3 {
16 | margin-top: 40px;
17 | }
18 | .footer {
19 | margin-top: 40px;
20 | }
21 |
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/index.html:
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1 |
2 |
3 |
4 | Toward Natural and Intelligible Speech Synthesis: An Empirical Study on Transfer Learning
8 |
9 |
10 | Toward Natural and Intelligible Speech Synthesis: An Empirical Study on Transfer Learning
11 | Authors: Jeewoo Yoon, Seong Choi, Taihu Li, and Jinyoung Han*
12 | Abstract: To synthesize natural and intelligible speech with a small amount of data, transfer learning with well-maintained and pre-trained data has been known to be useful. However, little attention has been paid to answer the following research questions with empirically-grounded evidence, ``How much pre-trained (source) speech data (e.g., 10 K utterances or 10 hours) used in transfer learning is enough for generating natural and intelligible speech?'' and ``For generating natural and intelligible speech, how much (target) speech data should at least be provided?'', which are essential for the quality of speech synthesis. To answer these questions, this paper conducts extensive experiments on speech synthesis with multiple source and target data with different lengths, speakers, and languages. We show that intelligible and natural speech can be synthesized with only 500 utterances of target data using transfer learning. Our work also reveals that at least 5000 utterances of source pre-trained data are required to synthesize decent speech.
13 | Experiment 1: Using different target datasets with different lengths
14 | The model was pre-trained with 10K utterances and fine-tuned with 250 and 500 utterances
16 | "I was so drunk last night that I can not remember anything."
17 |
18 |
19 |
E-SPK1-250
20 |
21 |
22 |
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E-SPK2-250
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27 |
28 |
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E-SPK3-250
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E-SPK1-500
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E-SPK2-500
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E-SPK3-500
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55 |
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"Tell me if you need anything."
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E-SPK1-250
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E-SPK2-250
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E-SPK3-250
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E-SPK1-500
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E-SPK2-500
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E-SPK3-500
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99 |
"이 옷 정말 예쁘지 않니?"
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K-SPK1-250
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K-SPK2-250
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K-SPK3-250
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K-SPK1-500
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K-SPK2-500
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K-SPK3-500
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"요즘에는 예식장 구하기가 하늘의 별 따기야."
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K-SPK1-250
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K-SPK2-250
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K-SPK3-250
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K-SPK1-500
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K-SPK2-500
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K-SPK3-500
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"气质这块拿捏得死死的"
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C-SPK1-250
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C-SPK2-250
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C-SPK3-250
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C-SPK1-500
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C-SPK2-500
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C-SPK3-500
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"他的写作水平不敢恭维"
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C-SPK1-250
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C-SPK2-250
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C-SPK3-250
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C-SPK1-500
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C-SPK2-500
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C-SPK3-500
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267 |
Experiment 2: Using different source datasets with different lengths
272 | The model was pre-trained with 5K utterances and fine-tuned with 500 utterances
274 | "I was so drunk last night that I can not remember anything."
275 |
276 |
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E-SPK1-500
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E-SPK2-500
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E-SPK3-500
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"요즘에는 예식장 구하기가 하늘의 별 따기야."
297 |
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K-SPK1-500
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K-SPK2-500
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K-SPK3-500
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"气质这块拿捏得死死的"
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C-SPK1-500
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C-SPK2-500
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C-SPK3-500
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