Auto-Landmark: Acoustic Landmark Dataset and Open-Source Toolkit for Landmark Extraction
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916753203789824 |
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| author | Zhang, Xiangyu Liu, Daijiao Xiao, Tianyi Xiao, Cihan Szalay, Tuende Shahin, Mostafa Ahmed, Beena Epps, Julien |
| author_facet | Zhang, Xiangyu Liu, Daijiao Xiao, Tianyi Xiao, Cihan Szalay, Tuende Shahin, Mostafa Ahmed, Beena Epps, Julien |
| contents | In the speech signal, acoustic landmarks identify times when the acoustic manifestations of the linguistically motivated distinctive features are most salient. Acoustic landmarks have been widely applied in various domains, including speech recognition, speech depression detection, clinical analysis of speech abnormalities, and the detection of disordered speech. However, there is currently no dataset available that provides precise timing information for landmarks, which has been proven to be crucial for downstream applications involving landmarks. In this paper, we selected the most useful acoustic landmarks based on previous research and annotated the TIMIT dataset with them, based on a combination of phoneme boundary information and manual inspection. Moreover, previous landmark extraction tools were not open source or benchmarked, so to address this, we developed an open source Python-based landmark extraction tool and established a series of landmark detection baselines. The first of their kinds, the dataset with landmark precise timing information, landmark extraction tool and baselines are designed to support a wide variety of future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07969 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Auto-Landmark: Acoustic Landmark Dataset and Open-Source Toolkit for Landmark Extraction Zhang, Xiangyu Liu, Daijiao Xiao, Tianyi Xiao, Cihan Szalay, Tuende Shahin, Mostafa Ahmed, Beena Epps, Julien Audio and Speech Processing In the speech signal, acoustic landmarks identify times when the acoustic manifestations of the linguistically motivated distinctive features are most salient. Acoustic landmarks have been widely applied in various domains, including speech recognition, speech depression detection, clinical analysis of speech abnormalities, and the detection of disordered speech. However, there is currently no dataset available that provides precise timing information for landmarks, which has been proven to be crucial for downstream applications involving landmarks. In this paper, we selected the most useful acoustic landmarks based on previous research and annotated the TIMIT dataset with them, based on a combination of phoneme boundary information and manual inspection. Moreover, previous landmark extraction tools were not open source or benchmarked, so to address this, we developed an open source Python-based landmark extraction tool and established a series of landmark detection baselines. The first of their kinds, the dataset with landmark precise timing information, landmark extraction tool and baselines are designed to support a wide variety of future research. |
| title | Auto-Landmark: Acoustic Landmark Dataset and Open-Source Toolkit for Landmark Extraction |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.07969 |