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Modeling the effect of military oxygen masks on speech characteristics
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In: Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03325087 ; Interspeech 2021, Aug 2021, Brno, Czech Republic (2021)
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Simulating reading mistakes for child speech Transformer-based phone recognition
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In: Annual Conference of the International Speech Communication Association (INTERSPEECH) ; https://hal.archives-ouvertes.fr/hal-03257870 ; Annual Conference of the International Speech Communication Association (INTERSPEECH), Aug 2021, Brno, Czech Republic (2021)
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Abstract:
International audience ; Current performance of automatic speech recognition (ASR) for children is below that of the latest systems dedicated to adult speech. Child speech is particularly difficult to recognise, and substantial corpora are missing to train acoustic models. Furthermore, in the scope of our reading assistant for 5-8-year-old children learning to read, models need to cope with disfluencies and reading mistakes, which remain considerable challenges even for state-of-the-art ASR systems. In this paper, we adapt an end-to-end Transformer acoustic model to speech from children learning to read. Transfer learning (TL) with a small amount of child speech improves the phone error rate (PER) by 48.7% relative over an adult model and outperforms a TL-adapted DNN-HMM model by 21.0% relative PER. Multi-objective training with a Connectionist Temporal Classification (CTC) function further reduces the PER by 4.8% relative. We propose a method of reading mistakes data augmentation, where we simulate word-level repetitions and substitutions with phonetically or graphically close words. Combining these two types of reading mistakes reaches a 19.9% PER, with a 13.1% relative improvement over the baseline. A detailed analysis shows that both the CTC multi-objective training and the augmentation with synthetic repetitions help the attention mechanisms better detect children's disfluencies.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; child speech; connectionist temporal classification; data augmentation; synthetic reading mistakes; transformer
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URL: https://hal.archives-ouvertes.fr/hal-03257870/document
https://hal.archives-ouvertes.fr/hal-03257870/file/Paper_Interspeech2021_LucileGelin.pdf
https://hal.archives-ouvertes.fr/hal-03257870
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A Data Augmentation Approach for Sign-Language-To-Text Translation In-The-Wild ...
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Effekten av textaugmenteringsstrategier på träffsäkerhet, F1-värde och viktat F1-värde ; The effect of text data augmentation strategies on Accuracy, F1-score, and weighted F1-score
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Using Data Augmentation and Time-Scale Modification to Improve ASR of Children’s Speech in Noisy Environments
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In: Applied Sciences ; Volume 11 ; Issue 18 (2021)
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Generating Synthetic Disguised Faces with Cycle-Consistency Loss and an Automated Filtering Algorithm
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In: Mathematics; Volume 10; Issue 1; Pages: 4 (2021)
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Volumetric changes at implant sites: A systematic appraisal of traditional methods and optical scanning- based digital technologies
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Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
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