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20232026
most citedListen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models

7 citations · 9 across the 20 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CL2024

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…

cs.CL2024

Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks

Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang +77

Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spo…

eess.AS2024★ 7 cited

Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models

Yi-Cheng Lin, Tzu-Quan Lin, Chih-Kai Yang +4

Speech Integrated Large Language Models (SILLMs) combine large language models with speech perception to perform diverse tasks, such as emotion recognition to speaker verification,…

eess.AS2024

Speech-Copilot: Leveraging Large Language Models for Speech Processing via Task Decomposition, Modularization, and Program Generation

Chun-Yi Kuan, Chih-Kai Yang, Wei-Ping Huang +2

In this work, we introduce Speech-Copilot, a modular framework for instruction-oriented speech-processing tasks that minimizes human effort in toolset construction. Unlike end-to-e…

cs.CL2024

Do Prompts Really Prompt? Exploring the Prompt Understanding Capability of Whisper

Chih-Kai Yang, Kuan-Po Huang, Hung-yi Lee

This research explores how the information of prompts interacts with the high-performing speech recognition model, Whisper. We compare its performances when prompted by prompts wit…