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Found 5 papers, 2 papers with code
Chain of Correction for Full-text Speech Recognition with Large Language Models
Full-text error correction with Large Language Models (LLMs) for Automatic Speech Recognition (ASR) has gained increased attention due to its potential to correct errors across long contexts and address a broader spectrum of error types, including punctuation restoration and inverse text normalization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Full-text Error Correction for Chinese Speech Recognition with Large Language Model
This paper investigates the effectiveness of LLMs for error correction in full-text generated by ASR systems from longer speech recordings, such as transcripts from podcasts, news broadcasts, and meetings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+10
Pinyin Regularization in Error Correction for Chinese Speech Recognition with Large Language Models
Firstly, we construct a specialized benchmark dataset aimed at error correction for Chinese ASR with 724K hypotheses-transcription pairs, named the Chinese Hypotheses Paradise dataset (ChineseHP), which contains a wide range of scenarios and presents significant challenges.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
TEA-PSE 3.0: Tencent-Ethereal-Audio-Lab Personalized Speech Enhancement System For ICASSP 2023 DNS Challenge
This paper introduces the Unbeatable Team's submission to the ICASSP 2023 Deep Noise Suppression (DNS) Challenge.
INTERSPEECH 2021 ConferencingSpeech Challenge: Towards Far-field Multi-Channel Speech Enhancement for Video Conferencing
The ConferencingSpeech 2021 challenge is proposed to stimulate research on far-field multi-channel speech enhancement for video conferencing.
