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Color backdoor: A robust poisoning attack in color space
Backdoor attacks against neural networks have been intensively investigated, where the
adversary compromises the integrity of the victim model, causing it to make wrong …
adversary compromises the integrity of the victim model, causing it to make wrong …
Hyporadise: An open baseline for generative speech recognition with large language models
C Chen, Y Hu, CHH Yang… - Advances in …, 2023 - proceedings.neurips.cc
Advancements in deep neural networks have allowed automatic speech recognition (ASR)
systems to attain human parity on several publicly available clean speech datasets …
systems to attain human parity on several publicly available clean speech datasets …
Can generative large language models perform asr error correction?
ASR error correction is an interesting option for post processing speech recognition system
outputs. These error correction models are usually trained in a supervised fashion using the …
outputs. These error correction models are usually trained in a supervised fashion using the …
Large language models are efficient learners of noise-robust speech recognition
Recent advances in large language models (LLMs) have promoted generative error
correction (GER) for automatic speech recognition (ASR), which leverages the rich linguistic …
correction (GER) for automatic speech recognition (ASR), which leverages the rich linguistic …
N-best t5: Robust asr error correction using multiple input hypotheses and constrained decoding space
Error correction models form an important part of Automatic Speech Recognition (ASR) post-
processing to improve the readability and quality of transcriptions. Most prior works use the 1 …
processing to improve the readability and quality of transcriptions. Most prior works use the 1 …
A robust semantic text communication system
Semantic communication is increasingly viewed as a promising solution to improve the
transmission efficiency. However, semantic communications are susceptible not only to …
transmission efficiency. However, semantic communications are susceptible not only to …
Softcorrect: Error correction with soft detection for automatic speech recognition
Error correction in automatic speech recognition (ASR) aims to correct those incorrect words
in sentences generated by ASR models. Since recent ASR models usually have low word …
in sentences generated by ASR models. Since recent ASR models usually have low word …
GenTranslate: Large language models are generative multilingual speech and machine translators
Recent advances in large language models (LLMs) have stepped forward the development
of multilingual speech and machine translation by its reduced representation errors and …
of multilingual speech and machine translation by its reduced representation errors and …
Mf-aed-aec: Speech emotion recognition by leveraging multimodal fusion, asr error detection, and asr error correction
The prevalent approach in speech emotion recognition (SER) involves integrating both
audio and textual information to comprehensively identify the speaker's emotion, with the …
audio and textual information to comprehensively identify the speaker's emotion, with the …
Improving Seq2Seq grammatical error correction via decoding interventions
The sequence-to-sequence (Seq2Seq) approach has recently been widely used in
grammatical error correction (GEC) and shows promising performance. However, the …
grammatical error correction (GEC) and shows promising performance. However, the …