5 papers
Toward Elastic Speech Inference: Training-Free Wake-Word Detection from Pretrained ASR
Hwayeon Kim, Youngwon Choi, Hyeonyu Kim
Recent ASR development has placed growing emphasis on generalization across diverse domains and acoustic conditions. Existing approaches typically adapt pretrained ASR models to fr…
Do Spoken Language Models Hear Speech as They Read Text? Bridging Structural Gaps Between Speech and Text
Hyeonyu Kim, Hwayeon Kim, Youngwon Choi +2
Spoken Language Models (SLMs) generate textual responses directly from speech, offering an alternative to cascaded systems. Despite recent advances, existing SLMs still exhibit wea…
ZeSTA: Zero-Shot TTS Augmentation with Domain-Conditioned Training for Data-Efficient Personalized Speech Synthesis
Youngwon Choi, Jinwoo Oh, Hwayeon Kim +1
We investigate the use of zero-shot text-to-speech (ZS-TTS) as a data augmentation source for low-resource personalized speech synthesis. While synthetic augmentation can provide l…
Exploring Fine-Tuning of Large Audio Language Models for Spoken Language Understanding under Limited Speech Data
Youngwon Choi, Jaeyoon Jung, Hyeonyu Kim +2
Large Audio Language Models (LALMs) have emerged as powerful tools for speech-related tasks but remain underexplored for fine-tuning, especially with limited speech data. To bridge…
DESAMO: A Device for Elder-Friendly Smart Homes Powered by Embedded LLM with Audio Modality
Youngwon Choi, Donghyuk Jung, Hwayeon Kim
We present DESAMO, an on-device smart home system for elder-friendly use powered by Audio LLM, that supports natural and private interactions. While conventional voice assistants r…