15 citations · 22 across the 12 of their papers we have counts for
10 papers · 1 filter
MDM-ASR: Bridging Accuracy and Efficiency in ASR with Diffusion-Based Non-Autoregressive Decoding
Hao Yen, Pin-Jui Ku, Ante Jukić +1
In sequence-to-sequence Transformer ASR, autoregressive (AR) models achieve strong accuracy but suffer from slow decoding, while non-autoregressive (NAR) models enable parallel dec…
Discrete Diffusion for Generative Modeling of Text-Aligned Speech Tokens
Pin-Jui Ku, He Huang, Jean-Marie Lemercier +3
This paper introduces a discrete diffusion model (DDM) framework for text-aligned speech tokenization and reconstruction. By replacing the auto-regressive speech decoder with a dis…
A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR
Hao Yen, Pin-Jui Ku, Sabato Marco Siniscalchi +1
We propose a bottom-up framework for automatic speech recognition (ASR) in syllable-based languages by unifying language-universal articulatory attribute modeling with syllable-lev…
An Investigation on Combining Geometry and Consistency Constraints into Phase Estimation for Speech Enhancement
Chun-Wei Ho, Pin-Jui Ku, Hao Yen +3
We propose a novel iterative phase estimation framework, termed multi-source Griffin-Lim algorithm (MSGLA), for speech enhancement (SE) under additive noise conditions. The core id…
Generative Speech Foundation Model Pretraining for High-Quality Speech Extraction and Restoration
Pin-Jui Ku, Alexander H. Liu, Roman Korostik +3
This paper proposes a generative pretraining foundation model for high-quality speech restoration tasks. By directly operating on complex-valued short-time Fourier transform coeffi…
An Explicit Consistency-Preserving Loss Function for Phase Reconstruction and Speech Enhancement
Pin-Jui Ku, Chun-Wei Ho, Hao Yen +2
In this work, we propose a novel consistency-preserving loss function for recovering the phase information in the context of phase reconstruction (PR) and speech enhancement (SE).…
Language-Universal Speech Attributes Modeling for Zero-Shot Multilingual Spoken Keyword Recognition
Hao Yen, Pin-Jui Ku, Sabato Marco Siniscalchi +1
We propose a novel language-universal approach to end-to-end automatic spoken keyword recognition (SKR) leveraging upon (i) a self-supervised pre-trained model, and (ii) a set of u…
A Multi-dimensional Deep Structured State Space Approach to Speech Enhancement Using Small-footprint Models
Pin-Jui Ku, Chao-Han Huck Yang, Sabato Marco Siniscalchi +1
We propose a multi-dimensional structured state space (S4) approach to speech enhancement. To better capture the spectral dependencies across the frequency axis, we focus on modify…
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
Hao Yen, Pin-Jui Ku, Chao-Han Huck Yang +4
In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system. The AR procedure aims to mo…
The AS-NU System for the M2VoC Challenge
Cheng-Hung Hu, Yi-Chiao Wu, Wen-Chin Huang +6
This paper describes the AS-NU systems for two tracks in MultiSpeaker Multi-Style Voice Cloning Challenge (M2VoC). The first track focuses on using a small number of 100 target utt…