3 citations · 5 across the 14 of their papers we have counts for
5 papers · 1 filter
Teaching Audio-Aware Large Language Models What Does Not Hear: Mitigating Hallucinations through Synthesized Negative Samples
Chun-Yi Kuan, Hung-yi Lee
Recent advancements in audio-aware large language models (ALLMs) enable them to process and understand audio inputs. However, these models often hallucinate non-existent sound even…
Speech-IFEval: Evaluating Instruction-Following and Quantifying Catastrophic Forgetting in Speech-Aware Language Models
Ke-Han Lu, Chun-Yi Kuan, Hung-yi Lee
We introduce Speech-IFeval, an evaluation framework designed to assess instruction-following capabilities and quantify catastrophic forgetting in speech-aware language models (SLMs…
From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data
Chun-Yi Kuan, Hung-yi Lee
Audio-aware large language models (ALLMs) have recently made great strides in understanding and processing audio inputs. These models are typically adapted from text-based large la…
Gender Bias in Instruction-Guided Speech Synthesis Models
Chun-Yi Kuan, Hung-yi Lee
Recent advancements in controllable expressive speech synthesis, especially in text-to-speech (TTS) models, have allowed for the generation of speech with specific styles guided by…
Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization
Tzu-Quan Lin, Wei-Ping Huang, Hao Tang +1
Fine-tuning speech representation models can enhance performance on specific tasks but often compromises their cross-task generalization ability. This degradation is often caused b…