no code implementations • 26 Jul 2024 • Junseok Ahn, Youkyum Kim, Yeunju Choi, Doyeop Kwak, Ji-Hoon Kim, Seongkyu Mun, Joon Son Chung
This paper introduces VoxSim, a dataset of perceptual voice similarity ratings.
no code implementations • 2 Nov 2020 • Yeunju Choi, Youngmoon Jung, Youngjoo Suh, Hoirin Kim
Although recent neural text-to-speech (TTS) systems have achieved high-quality speech synthesis, there are cases where a TTS system generates low-quality speech, mainly caused by limited training data or information loss during knowledge distillation.
no code implementations • 6 Oct 2020 • Youngmoon Jung, Yeunju Choi, Hyungjun Lim, Hoirin Kim
At the same time, there is an increasing requirement for an SV system: it should be robust to short speech segments, especially in noisy and reverberant environments.
no code implementations • 9 Aug 2020 • Yeunju Choi, Youngmoon Jung, Hoirin Kim
While deep learning has made impressive progress in speech synthesis and voice conversion, the assessment of the synthesized speech is still carried out by human participants.
no code implementations • 16 Jul 2020 • Yeunju Choi, Youngmoon Jung, Hoirin Kim
In this paper, we propose a multi-task learning (MTL) method to improve the performance of a MOS prediction model using the following two auxiliary tasks: spoofing detection (SD) and spoofing type classification (STC).
no code implementations • 7 Apr 2020 • Youngmoon Jung, Seong Min Kye, Yeunju Choi, Myunghun Jung, Hoirin Kim
In this approach, we obtain a speaker embedding vector by pooling single-scale features that are extracted from the last layer of a speaker feature extractor.
no code implementations • 26 Sep 2019 • Youngmoon Jung, Yeunju Choi, Hoirin Kim
The first approach is soft VAD, which performs a soft selection of frame-level features extracted from a speaker feature extractor.
no code implementations • 19 Jun 2019 • Youngmoon Jung, Younggwan Kim, Hyungjun Lim, Yeunju Choi, Hoirin Kim
Furthermore, we apply deep length normalization by augmenting the loss function with ring loss.