Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911006330978304 |
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| author | Ye, Zongli Lian, Jiachen Zhou, Xuanru Zhang, Jinming Li, Haodong Li, Shuhe Guo, Chenxu Das, Anaisha Park, Peter Ezzes, Zoe Vonk, Jet Morin, Brittany Bogley, Rian Wauters, Lisa Miller, Zachary Gorno-Tempini, Maria Anumanchipalli, Gopala |
| author_facet | Ye, Zongli Lian, Jiachen Zhou, Xuanru Zhang, Jinming Li, Haodong Li, Shuhe Guo, Chenxu Das, Anaisha Park, Peter Ezzes, Zoe Vonk, Jet Morin, Brittany Bogley, Rian Wauters, Lisa Miller, Zachary Gorno-Tempini, Maria Anumanchipalli, Gopala |
| contents | Accurate alignment of dysfluent speech with intended text is crucial for automating the diagnosis of neurodegenerative speech disorders. Traditional methods often fail to model phoneme similarities effectively, limiting their performance. In this work, we propose Neural LCS, a novel approach for dysfluent text-text and speech-text alignment. Neural LCS addresses key challenges, including partial alignment and context-aware similarity mapping, by leveraging robust phoneme-level modeling. We evaluate our method on a large-scale simulated dataset, generated using advanced data simulation techniques, and real PPA data. Neural LCS significantly outperforms state-of-the-art models in both alignment accuracy and dysfluent speech segmentation. Our results demonstrate the potential of Neural LCS to enhance automated systems for diagnosing and analyzing speech disorders, offering a more accurate and linguistically grounded solution for dysfluent speech alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12073 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis Ye, Zongli Lian, Jiachen Zhou, Xuanru Zhang, Jinming Li, Haodong Li, Shuhe Guo, Chenxu Das, Anaisha Park, Peter Ezzes, Zoe Vonk, Jet Morin, Brittany Bogley, Rian Wauters, Lisa Miller, Zachary Gorno-Tempini, Maria Anumanchipalli, Gopala Audio and Speech Processing Artificial Intelligence Computation and Language Sound Accurate alignment of dysfluent speech with intended text is crucial for automating the diagnosis of neurodegenerative speech disorders. Traditional methods often fail to model phoneme similarities effectively, limiting their performance. In this work, we propose Neural LCS, a novel approach for dysfluent text-text and speech-text alignment. Neural LCS addresses key challenges, including partial alignment and context-aware similarity mapping, by leveraging robust phoneme-level modeling. We evaluate our method on a large-scale simulated dataset, generated using advanced data simulation techniques, and real PPA data. Neural LCS significantly outperforms state-of-the-art models in both alignment accuracy and dysfluent speech segmentation. Our results demonstrate the potential of Neural LCS to enhance automated systems for diagnosing and analyzing speech disorders, offering a more accurate and linguistically grounded solution for dysfluent speech alignment. |
| title | Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language Sound |
| url | https://arxiv.org/abs/2506.12073 |