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cs.CL2024
XCB: an effective contextual biasing approach to bias cross-lingual phrases in speech recognition
Xucheng Wan, Naijun Zheng, Kai Liu +1
Contextualized ASR models have been demonstrated to effectively improve the recognition accuracy of uncommon phrases when a predefined phrase list is available. However, these mode…
cs.SD2024
An efficient text augmentation approach for contextualized Mandarin speech recognition
Naijun Zheng, Xucheng Wan, Kai Liu +2
Although contextualized automatic speech recognition (ASR) systems are commonly used to improve the recognition of uncommon words, their effectiveness is hindered by the inherent l…
eess.AS2024
MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition
Bingshen Mu, Yangze Li, Qijie Shao +5
Despite notable advancements in automatic speech recognition (ASR), performance tends to degrade when faced with adverse conditions. Generative error correction (GER) leverages the…