Papers with speech recognition system
Meta-Transfer Learning for Code-Switched Speech Recognition (2020.acl-main)
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| Challenge: | Increasing number of people in the world today speak a mixed-language as a result of being multilingual. |
| Approach: | They propose a method to transfer learn on a code-switched speech recognition system by extracting information from high-resource monolingual datasets. |
| Outcome: | The proposed model outperforms baselines on speech recognition and language modeling tasks and is faster to converge. |
Open ASR for Icelandic: Resources and a Baseline System (L18-1)
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| Challenge: | Existing language resources are not sufficient for less-resourced languages, but a system with sufficient resources is needed. |
| Approach: | They describe available language resources and their preparation for use in a large vocabulary speech recognition system for Icelandic. |
| Outcome: | The proposed system improves on acoustic training sets and a speech corpus with a pronunciation dictionary. |
Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors (2023.emnlp-main)
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| Challenge: | proposed method combines back transcription with fine-grained technique for categorizing speech recognition errors . proposed method relies on the use of synthesized speech in place of audio recording . |
| Approach: | They propose a method for investigating the impact of speech recognition errors on NLU models . they use a back transcription procedure and a fine-grained technique for categorizing errors . |
| Outcome: | The proposed method relies on synthesized speech in place of audio recording to evaluate the model. |
ATC-ANNO: Semantic Annotation for Air Traffic Control with Assistive Auto-Annotation (2020.lrec-1)
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| Challenge: | ATC communications are a challenging domain for automatic speech recognition (ASR) due to the time-sensitive nature of their task, annotators must have prior experience with ATC communication. |
| Approach: | They propose a tool for the transcription and semantic annotation of air traffic communications. |
| Outcome: | The proposed tool can annotate four times as many utterances in a single time. |
Automatic Speech Recognition for Uyghur through Multilingual Acoustic Modeling (2020.lrec-1)
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| Challenge: | Low-resource languages suffer from lower performance of Automatic Speech Recognition (ASR) due to the lack of data. |
| Approach: | They propose to use Turkish as donor language to train acoustic models using multilingual training to achieve more context coverage. |
| Outcome: | The proposed system performs better with multilingual training for the under-resourced Uyghur language. |
Improving Speech Recognition for the Elderly: A New Corpus of Elderly Japanese Speech and Investigation of Acoustic Modeling for Speech Recognition (2020.lrec-1)
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| Challenge: | In an aging society, a highly accurate speech recognition system is needed for use in electronic devices for the elderly but this cannot be achieved using conventional speech recognition systems due to the unique features of the speech of elderly people. |
| Approach: | They construct a new corpus of elderly Japanese speech from existing Japanese speech corpora and train them using existing data. |
| Outcome: | The proposed models achieve word error rates (WER) as low as 13.38%, exceeding the results of the previous study. |