Assisted subtitling: a new opportunity for access services

Aliprandi, Carlo; Gallucci, I.; Piccinini, Nicola; Raffaelli, Matteo; Pozo, A. del; Alvarez, Ariane Alvarez; Cassaca, Renato; Neto, João N.; Mendes, César Miranda; Viveiros, Márcio

doi:10.1049/ib.2014.0020

Cited by 1 publication

(1 citation statement)

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“…Learning from training data, the ASR system can match human transcribers for some critical tasks. It has been widely applied in several areas, such as live captioning, intelligent appliance control (Aliprandi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

New Datasets and Controllable Iterative Data Augmentation Method for Code-switching ASR Error Correction

Wan,

Peng

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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With the wide use of automatic speech recognition(ASR) systems, researchers pay more attention to the ASR error correction task to improve the quality of recognition results. In particular, ASR in bilingual or multilingual settings, namely code-switching ASR, has greater challenges and research value. In this paper, we first present code-switching ASR correction datasets obtained from solid ASR systems and automatic annotators. The datasets contain Chinese-English code-switching dialogues of bilingual speakers in Singapore, Malaysia, and Hong Kong. Based on this task, we propose a controllable iterative (CI) data augmentation method for improving the performance of mainstream ASR error correction systems. With a small amount of training data, our proposed method has the ability to iteratively produce abundant pseudo parallel data from the monolingual corpus for Chinese-English code-switching ASR correction. Results of experiments show that our method achieves the best performance compared with the rulebased, back-translation-based data augmentation methods and large language model Chat-GPT.

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Section: Introductionmentioning

confidence: 99%