Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network
In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN).
The recently proposed LPCNet vocoder has successfully achieved high-quality and lightweight speech synthesis systems by combining a vocal tract LP filter with a WaveRNN-based vocal source (i.e., excitation) generator.
However, the quality of synthesized speech is often unstable because the vocal source component is insufficiently represented by the mu-law quantization method, and the model is trained without considering the entire speech production mechanism.
To address this problem, we first introduce LP-MDN, which enables the autoregressive neural vocoder to structurally represent the interactions between the vocal tract and vocal source components.
Then, we propose to incorporate the LP-MDN to the LPCNet vocoder by replacing the conventional discretized output with continuous density distribution.
The experimental results verify that the proposed system provides high quality synthetic speech by achieving a mean opinion score of 4.41 within a text-to-speech framework.
|294||International Conference||Seyun Um, Sangshin Oh, Kyungguen Byun, Inseon Jang, ChungHyun Ahn, Hong-Goo Kang "Emotional Speech Synthesis with Rich and Granularized Control" in ICASSP, 2020|
|293||International Conference||Min-Jae Hwang, Eunwoo Song, Ryuichi Yamamoto, Frank Soong, Hong-Goo Kang "Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network" in ICASSP, 2020|
|292||International Journal||Soo-Whan Chung, Joon Son Chung, Hong Goo Kang "Perfect Match: Self-Supervised Embeddings for Cross-Modal Retrieval" in IEEE Journal of Selected Topics in Signal Processing, vol.14, issue 3, 2020|
|291||International Conference||Hyeonjoo Kang, Young-Sun Joo, Inseon Jang, Chunghyun Ahn, Hong-Goo Kang "A Study on Acoustic Parameter Selection Strategies to Improve Deep Learning-Based Speech Synthesis" in APSIPA, 2019|
|290||International Journal||Ohsung Kwon, Inseon Jang, ChungHyun Ahn, Hong-Goo Kang "An Effective Style Token Weight Control Technique for End-to-End Emotional Speech Synthesis" in IEEE Signal Processing Letters, vol.26, issue 9, pp.1383-1387, 2019|
|289||International Conference||Min-Jae Hwang, Hong-Goo Kang "Parameter enhancement for MELP speech codec in noisy communication environment" in INTERSPEECH, 2019|
|288||Domestic Journal||오상신, 엄세연, 장인선, 안충현, 강홍구 "k-평균 알고리즘을 활용한 음성의 대표 감정 스타일 결정 방법" in 한국음향학회지, vol.38, 제 5호, pp.614-620, 2019|
|287||International Journal||Jinkyu Lee, Hong-Goo Kang "A Joint Learning Algorithm for Complex-Valued T-F Masks in Deep Learning-Based Single-Channel Speech Enhancement Systems" in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.27, issue 6, pp.1098-1108, 2019|
|286||International Conference||Keulbit Kim, Jinkyu Lee, Jan Skoglund, Hong-Goo Kang "Model Order Selection for Wind Noise Reduction in Non-negative Matrix Factorization" in ITC-CSCC, 2019|
|285||International Conference||Ohsung Kwon, Inseon Jang, ChungHyun Ahn, Hong-Goo Kang "Emotional Speech Synthesis Based on Style Embedded Tacotron2 Framework" in ITC-CSCC, 2019|