no code implementations • 17 Jan 2024 • Seung-bin Kim, Sang-Hoon Lee, Seong-Whan Lee
With this method, despite training exclusively on the target language's monolingual data, we can generate target language speech in the inference stage using language-agnostic speech embedding from the source language speech.
1 code implementation • 21 Nov 2023 • Sang-Hoon Lee, Ha-Yeong Choi, Seung-bin Kim, Seong-Whan Lee
Furthermore, we significantly improve the naturalness and speaker similarity of synthetic speech even in zero-shot speech synthesis scenarios.
no code implementations • 10 Jun 2020 • Hye-jin Shim, Jee-weon Jung, Ju-ho Kim, Seung-bin Kim, Ha-Jin Yu
In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection: an end-to-end monolithic approach and a back-end modular approach.
1 code implementation • 7 May 2020 • Seung-bin Kim, Jee-weon Jung, Hye-jin Shim, Ju-ho Kim, Ha-Jin Yu
The proposed method segments an input utterance into several short utterances and then aggregates the segment embeddings extracted from the segmented inputs to compose a speaker embedding.
2 code implementations • 1 Apr 2020 • Jee-weon Jung, Seung-bin Kim, Hye-jin Shim, Ju-ho Kim, Ha-Jin Yu
Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms.