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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.00007 |
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| _version_ | 1866913813558722560 |
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| author | Bandekar, Jesuraj Udupa, Sathvik Ghosh, Prasanta Kumar |
| author_facet | Bandekar, Jesuraj Udupa, Sathvik Ghosh, Prasanta Kumar |
| contents | We propose an approach for learning critical articulators for phonemes through a machine learning approach. We formulate the learning with three models trained end to end. First, we use Acoustic to Articulatory Inversion (AAI) to predict time-varying speech articulators EMA. We also predict the phoneme-specific weights across articulators for each frame. To avoid overfitting, we also add a dropout layer before the weights prediction layer. Next, we normalize the predicted weights across articulators using min-max normalization for each frame. The normalized weights are multiplied by the ground truth $EMA$ and then we try to predict the phones at each frame. We train this whole setup end to end and use two losses. One loss is for the phone prediction which is the cross entropy loss and the other is for the AAI prediction which is the mean squared error loss. To maintain gradient flow between the phone prediction block and the $EMA$ prediction block, we use straight-through estimation. The goal here is to predict the weights of the articulator at each frame while training the model end to end. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Discovering phoneme-specific critical articulators through a data-driven approach Bandekar, Jesuraj Udupa, Sathvik Ghosh, Prasanta Kumar Audio and Speech Processing We propose an approach for learning critical articulators for phonemes through a machine learning approach. We formulate the learning with three models trained end to end. First, we use Acoustic to Articulatory Inversion (AAI) to predict time-varying speech articulators EMA. We also predict the phoneme-specific weights across articulators for each frame. To avoid overfitting, we also add a dropout layer before the weights prediction layer. Next, we normalize the predicted weights across articulators using min-max normalization for each frame. The normalized weights are multiplied by the ground truth $EMA$ and then we try to predict the phones at each frame. We train this whole setup end to end and use two losses. One loss is for the phone prediction which is the cross entropy loss and the other is for the AAI prediction which is the mean squared error loss. To maintain gradient flow between the phone prediction block and the $EMA$ prediction block, we use straight-through estimation. The goal here is to predict the weights of the articulator at each frame while training the model end to end. |
| title | Discovering phoneme-specific critical articulators through a data-driven approach |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.00007 |