1 citations · 2 across the 6 of their papers we have counts for
7 papers
From Speech to Interaction: Analyzing Multimodal Systems in Cocktail-Party Scenarios
Thai-Binh Nguyen, Zhaolin Li, Jan Niehues +1
Humans have the remarkable ability to engage in spontaneous informal conversations and selectively attend to individual speakers while filtering out competing speech from nearby co…
Multimodal In-context Learning for ASR of Low-resource Languages
Zhaolin Li, Jan Niehues
Automatic speech recognition (ASR) still covers only a small fraction of the world's languages, mainly due to supervised data scarcity. In-context learning (ICL) with large languag…
In-context Language Learning for Endangered Languages in Speech Recognition
Zhaolin Li, Jan Niehues
With approximately 7,000 languages spoken worldwide, current large language models (LLMs) support only a small subset. Prior research indicates LLMs can learn new languages for cer…
KIT's Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization
Zhaolin Li, Yining Liu, Danni Liu +6
This paper presents KIT's submissions to the IWSLT 2025 low-resource track. We develop both cascaded systems, consisting of Automatic Speech Recognition (ASR) and Machine Translati…
Augmenting Automatic Speech Recognition Models with Disfluency Detection
Robin Amann, Zhaolin Li, Barbara Bruno +1
Speech disfluency commonly occurs in conversational and spontaneous speech. However, standard Automatic Speech Recognition (ASR) models struggle to accurately recognize these disfl…
Blending LLMs into Cascaded Speech Translation: KIT's Offline Speech Translation System for IWSLT 2024
Sai Koneru, Thai-Binh Nguyen, Ngoc-Quan Pham +4
Large Language Models (LLMs) are currently under exploration for various tasks, including Automatic Speech Recognition (ASR), Machine Translation (MT), and even End-to-End Speech T…
SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading
Tu Anh Dinh, Carlos Mullov, Leonard Bärmann +15
With the rapid development of Large Language Models (LLMs), it is crucial to have benchmarks which can evaluate the ability of LLMs on different domains. One common use of LLMs is…