3 citations · 7 across the 18 of their papers we have counts for
15 papers · 1 filter
Listen to the Latents: Self-Correcting Speech Recognition in Large Audio Language Models Through Hidden-State Interactions
Chan-Jan Hsu, Jaeyeon Kim, Chao-Han Huck Yang +3
Recent automatic speech recognition (ASR) systems increasingly integrate large language models (LLMs) to leverage their semantic knowledge, either externally through logit fusion o…
REDDIT: Forgetting-Resistant Correction of Timestamp Drift in ASR via Replay-Based Distribution Editing
Cheng-Kang Chou, Ming-To Chuang, Ke-Han Lu +2
Modern autoregressive ASR systems can emit timestamps as decoded tokens, enabling timestamped transcription without frame-level aligners or inference-time post-processing. We show…
A Self-Refining Framework for Enhancing ASR Using TTS-Synthesized Data
Cheng-Kang Chou, Chan-Jan Hsu, Ho-Lam Chung +5
We propose a self-refining framework that enhances ASR performance with only unlabeled datasets. The process starts with an existing ASR model generating pseudo-labels on unannotat…
The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on Llama with Vision-Aware and Function-Calling Capabilities
MediaTek Research, :, Chan-Jan Hsu +6
Llama-Breeze2 (hereinafter referred to as Breeze2) is a suite of advanced multi-modal language models, available in 3B and 8B parameter configurations, specifically designed to enh…
BreezyVoice: Adapting TTS for Taiwanese Mandarin with Enhanced Polyphone Disambiguation -- Challenges and Insights
Chan-Jan Hsu, Yi-Cheng Lin, Chia-Chun Lin +10
We present BreezyVoice, a Text-to-Speech (TTS) system specifically adapted for Taiwanese Mandarin, highlighting phonetic control abilities to address the unique challenges of polyp…
Building a Taiwanese Mandarin Spoken Language Model: A First Attempt
Chih-Kai Yang, Yu-Kuan Fu, Chen-An Li +18
This technical report presents our initial attempt to build a spoken large language model (LLM) for Taiwanese Mandarin, specifically tailored to enable real-time, speech-to-speech…
Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation
Yi-Chang Chen, Po-Chun Hsu, Chan-Jan Hsu +1
Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling. This research delves into enhancin…
FineWeb-zhtw: Scalable Curation of Traditional Chinese Text Data from the Web
Cheng-Wei Lin, Wan-Hsuan Hsieh, Kai-Xin Guan +6
The quality and size of a pretraining dataset significantly influence the performance of large language models (LLMs). While there have been numerous efforts in the curation of suc…
Let's Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Robust and Instruction-Aware ASR and OCR
Chan-Jan Hsu, Yi-Chang Chen, Feng-Ting Liao +4
We propose "Generative Fusion Decoding" (GFD), a novel shallow fusion framework designed to integrate large language models (LLMs) into cross-modal text recognition systems for aut…
Breeze-7B Technical Report
Chan-Jan Hsu, Chang-Le Liu, Feng-Ting Liao +3
Breeze-7B is an open-source language model based on Mistral-7B, designed to address the need for improved language comprehension and chatbot-oriented capabilities in Traditional Ch…
Advancing the Evaluation of Traditional Chinese Language Models: Towards a Comprehensive Benchmark Suite
Chan-Jan Hsu, Chang-Le Liu, Feng-Ting Liao +3
The evaluation of large language models is an essential task in the field of language understanding and generation. As language models continue to advance, the need for effective b…
Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results
Philipp Ennen, Po-Chun Hsu, Chan-Jan Hsu +6
In this paper we present the multilingual language model BLOOM-zh that features enhanced support for Traditional Chinese. BLOOM-zh has its origins in the open-source BLOOM models p…
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR
Jiatong Shi, Chan-Jan Hsu, Holam Chung +5
Spoken language understanding (SLU) is a task aiming to extract high-level semantics from spoken utterances. Previous works have investigated the use of speech self-supervised mode…
T5lephone: Bridging Speech and Text Self-supervised Models for Spoken Language Understanding via Phoneme level T5
Chan-Jan Hsu, Ho-Lam Chung, Hung-yi Lee +1
In Spoken language understanding (SLU), a natural solution is concatenating pre-trained speech models (e.g. HuBERT) and pretrained language models (PLM, e.g. T5). Most previous wor…
XDBERT: Distilling Visual Information to BERT from Cross-Modal Systems to Improve Language Understanding
Chan-Jan Hsu, Hung-yi Lee, Yu Tsao
Transformer-based models are widely used in natural language understanding (NLU) tasks, and multimodal transformers have been effective in visual-language tasks. This study explore…