Human Transcription Quality Improvement
Not on the map. Why?
Abstract (arXiv)
High quality transcription data is crucial for training automatic speech recognition (ASR) systems. However, the existing industry-level data collection pipelines are expensive to researchers, while the quality of crowdsourced transcription is low. In this paper, we propose a reliable method to collect speech transcriptions. We introduce two mechanisms to improve transcription quality: confidence estimation based reprocessing at labeling stage, and automatic word error correction at post-labeling stage. We collect and release LibriCrowd - a large-scale crowdsourced dataset of audio transcriptions on 100 hours of English speech. Experiment shows the Transcription WER is reduced by over 50%. We further investigate the impact of transcription error on ASR model performance and found a strong correlation. The transcription quality improvement provides over 10% relative WER reduction for ASR models. We release the dataset and code to benefit the research community.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 37 references that are in the map. First posted on arXiv 24 Sep 2023.
Builds on
19 of 37 references.
References · 19
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
- 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
- 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,609
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
- 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
- 2019Libri-Light: A Benchmark for ASR with Limited or No Supervisioncited by 462
- 2021W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Trainingcited by 340
- 2020Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Losscited by 232
7 more
- 2020ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Contextcited by 96
- 2020CTC-Segmentation of Large Corpora for German End-to-end Speech Recognitioncited by 75
- 2022RescoreBERT: Discriminative Speech Recognition Rescoring with BERTcited by 34
- 2021ASR Rescoring and Confidence Estimation with ELECTRAcited by 16
- 2021CrowdSpeech and VoxDIY: Benchmark Datasets for Crowdsourced Audio Transcriptioncited by 9
- 2021Investigation of Training Label Error Impact on RNN-Tcited by 3
- 2021Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Developmentcited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.