SpeechBlender: Speech Augmentation Framework for Mispronunciation Data Generation

• URL: http://arxiv.org/abs/2211.00923v3
• Date: Wed, 12 Jul 2023 12:28:56 GMT
• Title: SpeechBlender: Speech Augmentation Framework for Mispronunciation Data Generation
• Authors: Yassine El Kheir, Shammur Absar Chowdhury, Ahmed Ali, Hamdy Mubarak, and Shazia Afzal
• Abstract summary: SpeechBlender is a fine-grained data augmentation pipeline for generating mispronunciation errors. Our proposed technique achieves state-of-the-art results, with Speechocean762, on ASR dependent mispronunciation detection models.
• Score: 11.91301106502376
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The lack of labeled second language (L2) speech data is a major challenge in designing mispronunciation detection models. We introduce SpeechBlender - a fine-grained data augmentation pipeline for generating mispronunciation errors to overcome such data scarcity. The SpeechBlender utilizes varieties of masks to target different regions of phonetic units, and use the mixing factors to linearly interpolate raw speech signals while augmenting pronunciation. The masks facilitate smooth blending of the signals, generating more effective samples than the `Cut/Paste' method. Our proposed technique achieves state-of-the-art results, with Speechocean762, on ASR dependent mispronunciation detection models at phoneme level, with a 2.0% gain in Pearson Correlation Coefficient (PCC) compared to the previous state-of-the-art [1]. Additionally, we demonstrate a 5.0% improvement at the phoneme level compared to our baseline. We also observed a 4.6% increase in F1-score with Arabic AraVoiceL2 testset.

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