Table of Links Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 5.4 Evaluation Metrics For the reconstruction and resynthesis tasks, we conducted seven objective metrics: a log-scale Mel error distance (Mel), perceptual evaluation of speech quality (PESQ)[5] , Pitch, periodicity (Period.), voice/unvoice (V/UV) F1 score, and logscale F0 consistency F0c. We used the official implementation of CARGAN [60] for pitch, periodicity, and U/UV F1[6] . For F0c, we calculated the L1 distance between the log-scale ground-truth and the predicted F0 in the HAG. For VC, we used two subjective metrics: naturalness mean opinion score (nMOS) and voice similarity MOS (sMOS) with a CI of 95%; and three objective metrics for naturalness: UTMOS [69], character error rate (CER) and word error rate (WER); two objective metrics for similarity: automatic speaker verification equal error rate (EER), and speaker encoder cosine similarity (SECS). We utilized the open-source UTMOS[7] which is an MOS prediction model for a naturalness metric. Although this can not be considered an absolute evaluation metric, we believe that it is a simple way to estimate the audio quality of synthetic speech. Additionally, this method does not require ground-truth audio or labels to estimate the score. Therefore, we highly recommend using this simple metric during validation by adding a single line. For CER and WER, we utilized the Whisper’s official implementation. We used a large model with 1,550 M parameters and calculated the CER and WER after text normalization, as presented in the official implementation. We utilized a pre-trained automatic speaker verification models [42][8] which was trained with a large-scale speech dataset, VoxCeleb2 [14]. In [13], the effectiveness of metric learning in automatic speaker verification was demonstrated. Furthermore, [42] introduced online data augmentation, which decreased the EER from 2.17% to 1.17%. In addition, we utilized the pre-trained speaker encoder, Resemblyzer [9] to extract a speaker representation, and we calculated the cosine similarity between the speaker representation of the target speech and synthetic speech. For TTS, we additionally utilize a prosody MOS (pMOS). Sixty samples were randomly selected for each model. The nMOS was rated by 10 listeners on a scale of 1-5, and the sMOS and pMOS were rated by 10 listeners on a scale of 1-4. A confidence interval of 95% was reported for MOS. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. [5] https://github.com/ludlows/PESQ [6] https://github.com/descriptinc/cargan [7] https://github.com/tarepan/SpeechMOS [8] https://github.com/clovaai/voxceleb_trainer [9] https://github.com/resemble-ai/Resemblyzer Authors:
(1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.4 Speech Super-resolution 3.5 Model Architecture 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.4 Evaluation Metrics 5.5 Ablation Study 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 7 Conclusion, Acknowledgement and References 5.4 Evaluation Metrics For the reconstruction and resynthesis tasks, we conducted seven objective metrics: a log-scale Mel error distance (Mel), perceptual evaluation of speech quality (PESQ)[5] , Pitch, periodicity (Period.), voice/unvoice (V/UV) F1 score, and logscale F0 consistency F0c. We used the official implementation of CARGAN [60] for pitch, periodicity, and U/UV F1[6] . For F0c, we calculated the L1 distance between the log-scale ground-truth and the predicted F0 in the HAG. For VC, we used two subjective metrics: naturalness mean opinion score (nMOS) and voice similarity MOS (sMOS) with a CI of 95%; and three objective metrics for naturalness: UTMOS [69], character error rate (CER) and word error rate (WER); two objective metrics for similarity: automatic speaker verification equal error rate (EER), and speaker encoder cosine similarity (SECS). We utilized the open-source UTMOS[7] which is an MOS prediction model for a naturalness metric. Although this can not be considered an absolute evaluation metric, we believe that it is a simple way to estimate the audio quality of synthetic speech. Additionally, this method does not require ground-truth audio or labels to estimate the score. Therefore, we highly recommend using this simple metric during validation by adding a single line. For CER and WER, we utilized the Whisper’s official implementation. We used a large model with 1,550 M parameters and calculated the CER and WER after text normalization, as presented in the official implementation. We utilized a pre-trained automatic speaker verification models [42][8] which was trained with a large-scale speech dataset, VoxCeleb2 [14]. In [13], the effectiveness of metric learning in automatic speaker verification was demonstrated. Furthermore, [42] introduced online data augmentation, which decreased the EER from 2.17% to 1.17%. In addition, we utilized the pre-trained speaker encoder, Resemblyzer [9] to extract a speaker representation, and we calculated the cosine similarity between the speaker representation of the target speech and synthetic speech. For TTS, we additionally utilize a prosody MOS (pMOS). Sixty samples were randomly selected for each model. The nMOS was rated by 10 listeners on a scale of 1-5, and the sMOS and pMOS were rated by 10 listeners on a scale of 1-4. A confidence interval of 95% was reported for MOS. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv [5] https://github.com/ludlows/PESQ https://github.com/ludlows/PESQ [6] https://github.com/descriptinc/cargan https://github.com/descriptinc/cargan [7] https://github.com/tarepan/SpeechMOS https://github.com/tarepan/SpeechMOS [8] https://github.com/clovaai/voxceleb_trainer https://github.com/clovaai/voxceleb_trainer [9] https://github.com/resemble-ai/Resemblyzer https://github.com/resemble-ai/Resemblyzer Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Authors: Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author.

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