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20232026
most citedExploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages

1 citations · 1 across the 13 of their papers we have counts for

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cs.CL2026

Anchoring Speech with Semantics: A Multimodal Adapter Mechanism for Automatic Speech Recognition in Low-Resource Languages

Kuan-Tang Huang, Cheng-Yeh Yang, Chien-Chun Wang +3

Low-resource ASR remains difficult because scarce transcripts provide limited supervised evidence for target-side generation. To address this gap, we propose SAMA-ASR, a lightweigh…

eess.AS2026

Personalized Keyword Spotting for User-Defined Keywords Leveraging Text-Independent Speaker Verification

Ming-Hsiang Hu, Kuan-Tang Huang, Chien-Chun Wang +2

User-defined keyword spotting (UD-KWS) enables zero-shot wake-word detection from text, but existing systems learn speaker-invariant representations that cannot reject impostors ut…

q-fin.ST2026

Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion

Pei-Jun Liao, Hung-Shin Lee, Yao-Fei Cheng +3

Predicting stock prices presents challenges in financial forecasting. While traditional approaches such as ARIMA and RNNs are prevalent, recent developments in Large Language Model…

eess.AS2026

TG-ASR: Translation-Guided Learning with Parallel Gated Cross Attention for Low-Resource Automatic Speech Recognition

Cheng-Yeh Yang, Chien-Chun Wang, Li-Wei Chen +3

Low-resource automatic speech recognition (ASR) continues to pose significant challenges, primarily due to the limited availability of transcribed data for numerous languages. Whil…

eess.IV2026

SCENE: Semantic-aware Codec Enhancement with Neural Embeddings

Han-Yu Lin, Li-Wei Chen, Hung-Shin Lee

Compression artifacts from standard video codecs often degrade perceptual quality. We propose a lightweight, semantic-aware pre-processing framework that enhances perceptual fideli…