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Seung-Bin Kim

7 accepted papers

2026

Toward Complex-Valued Neural Networks for Waveform Generation

ICLR 2026poster

Neural vocoders have recently advanced waveform generation, yielding natural and expressive audio. Among these approaches, iSTFT-based vocoders have recently gained attention. They predict a complex-valued spectrogram and then synthesize the waveform via iSTFT, thereby avoiding learned upsampling st…

Cited by 0SourcecodeScholar
2025

FLowHigh: Towards Efficient and High-Quality Audio Super-Resolution with Single-Step Flow Matching

ICASSP 2025accepted

Audio super-resolution is challenging owing to its ill-posed nature. Recently, the application of diffusion models in audio super-resolution has shown promising results in alleviating this challenge. However, diffusion-based models have limitations, primarily the necessity for numerous sampling step…

Cited by 0SourceScholar
2025

FillerSpeech: Towards Human-Like Text-to-Speech Synthesis with Filler Insertion and Filler Style Control

EMNLP 2025

Recent advancements in speech synthesis have significantly improved the audio quality and pronunciation of synthesized speech. To further advance toward human-like conversational speech synthesis, this paper presents FillerSpeech, a novel speech synthesis framework that enables natural filler insert

2025

JELLY: Joint Emotion Recognition and Context Reasoning with LLMs for Conversational Speech Synthesis

ICASSP 2025accepted

Recently, there has been a growing demand for conversational speech synthesis (CSS) that generates more natural speech by considering the conversational context. To address this, we introduce JELLY, a novel CSS framework that integrates emotion recognition and context reasoning for generating approp…

Cited by 0SourceScholar
2024

TranSentence: speech-to-speech Translation via Language-Agnostic Sentence-Level Speech Encoding without Language-Parallel Data

ICASSP 2024accepted

Although there has been significant advancement in the field of speech-to-speech translation, conventional models still require language-parallel speech data between the source and target languages for training. In this paper, we introduce TranSentence, a novel speech-to-speech translation without l…

Cited by 0SourceScholar
2022

EMOQ-TTS: Emotion Intensity Quantization for Fine-Grained Controllable Emotional Text-to-Speech

ICASSP 2022accepted

Although recent advances in text-to-speech (TTS) have shown significant improvement, it is still limited to emotional speech synthesis. To produce emotional speech, most works utilize emotion information extracted from emotion labels or reference audio. However, they result in monotonous emotional e…

Cited by 0SourceScholar
2022

HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis

NeurIPS 2022accept

This paper presents HierSpeech, a high-quality end-to-end text-to-speech (TTS) system based on a hierarchical conditional variational autoencoder (VAE) utilizing self-supervised speech representations. Recently, single-stage TTS systems, which directly generate raw speech waveform from text, have be…

Cited by 57SourcePDFScholar