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Jaejin Cho

10 accepted papers

2025

MIB: Mixed Information Bottleneck for Out-of-Distribution Keyword Spotting

Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Jaejin Cho, Ching Hua Lee, Chouchang Yang, Yilin Shen +1
ICASSP 2025accepted

Deep Keyword Spotting (KWS) systems continuously process audio streams to detect keywords. However, performance of deep neural networks degrade when the input data diverges from the training data; referred to as Out-of-Distribution (OOD) data problem. In this paper, we show performance degradation o…

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2025

RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior

Ching-Hua Lee, Chouchang Yang, Jaejin Cho, Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Yilin Shen +1
ICML 2025poster

Denoising diffusion probabilistic models (DDPMs) can be utilized to recover a clean signal from its degraded observation(s) by conditioning the model on the degraded signal. The degraded signals are themselves contaminated versions of the clean signals; due to this correlation, they may encompass ce…

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2024

CIFD: Controlled Information Flow to Enhance Knowledge Distillation

Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Jaejin Cho, Ching-Hua Lee, Chouchang Yang, Yilin Shen +1
NeurIPS 2024poster

Knowledge Distillation is the mechanism by which the insights gained from a larger teacher model are transferred to a smaller student model. However, the transfer suffers when the teacher model is significantly larger than the student. To overcome this, prior works have proposed training intermediat…

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2024

End-To-End Personalized Cuff-Less Blood Pressure Monitoring Using ECG and PPG Signals

Suhas BN, Rakshith Sharma Srinivasa, Yashas Malur Saidutta, Jaejin Cho, Ching Hua Lee, Chouchang Yang +2
ICASSP 2024accepted

Cuffless blood pressure (BP) monitoring offers the potential for continuous, non-invasive healthcare but has been limited in adoption by existing models relying on handcrafted features from ECG and PPG signals. To overcome this, researchers have looked to deep learning. Along these lines, in this pa…

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2024

Leveraging Self-Supervised Speech Representations for Domain Adaptation in Speech Enhancement

Ching Hua Lee, Chouchang Yang, Rakshith Sharma Srinivasa, Yashas Malur Saidutta, Jaejin Cho, Yilin Shen +1
ICASSP 2024accepted

Deep learning based speech enhancement (SE) approaches could suffer from performance degradation due to mismatch between training and testing environments. A realistic situation is that an SE model trained on parallel noisy-clean utterances from one environment, the source domain, may fail to perfor…

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2024

Zero-Shot Intent Classification Using a Semantic Similarity Aware Contrastive Loss and Large Language Model

Jaejin Cho, Rakshith Sharma Srinivasa, Ching Hua Lee, Yashas Malur Saidutta, Chouchang Yang, Yilin Shen +1
ICASSP 2024accepted

Zero-shot systems can reduce the cost of collecting data and training in a new domain since they can work directly with the test data without further training. In this paper, we build zero-shot systems for intent classification, based on Semantic Similarity-aware Contrastive Loss (SSCL) that address…

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2023

CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss

Rakshith Sharma Srinivasa, Jaejin Cho, Chouchang Yang, Yashas Malur Saidutta, Ching-Hua Lee, Yilin Shen +1
NeurIPS 2023poster

This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-trained model in one modality is used for representation learning in another domain using pairwise data. The learnt models in the latter domain can then be used for a diverse set of tasks in a 0-shot way, similar…

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2021

Improving Reconstruction Loss Based Speaker Embedding in Unsupervised and Semi-Supervised Scenarios

Jaejin Cho, Piotr Zelasko, Jesús Villalba, Najim Dehak
ICASSP 2021accepted

Text-to-speech (TTS) models trained to minimize the spectrogram reconstruction loss can learn speaker embeddings without explicit speaker identity supervision, unlike x-vector speaker identification (SID) systems. Leveraging this way of speaker embedding learning can be useful in unsupervised or sem…

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2019

Language Model Integration Based on Memory Control for Sequence to Sequence Speech Recognition

Jaejin Cho, Shinji Watanabe, Takaaki Hori, Murali Karthick Baskar, Hirofumi Inaguma, Jesús Villalba +1
ICASSP 2019accepted

In this paper, we explore several new schemes to train a seq2seq model to integrate a pre-trained language model (LM). Our proposed fusion methods focus on the memory cell state and the hidden state in the seq2seq decoder long short-term memory (LSTM), and the memory cell state is updated by the LM…

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2019

Transfer Learning of Language-independent End-to-end ASR with Language Model Fusion

Hirofumi Inaguma, Jaejin Cho, Murali Karthick Baskar, Tatsuya Kawahara, Shinji Watanabe
ICASSP 2019accepted

This work explores better adaptation methods to low-resource languages using an external language model (LM) under the framework of transfer learning. We first build a language-independent ASR system in a unified sequence-to-sequence (S2S) architecture with a shared vocabulary among all languages. D…

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