The OCON model: an old but gold solution for distributable supervised classification
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913918100701184 |
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| author | Giacomelli, Stefano Giordano, Marco Rinaldi, Claudia |
| author_facet | Giacomelli, Stefano Giordano, Marco Rinaldi, Claudia |
| contents | This paper introduces to a structured application of the One-Class approach and the One-Class-One-Network model for supervised classification tasks, specifically addressing a vowel phonemes classification case study within the Automatic Speech Recognition research field. Through pseudo-Neural Architecture Search and Hyper-Parameters Tuning experiments conducted with an informed grid-search methodology, we achieve classification accuracy comparable to nowadays complex architectures (90.0 - 93.7%). Despite its simplicity, our model prioritizes generalization of language context and distributed applicability, supported by relevant statistical and performance metrics. The experiments code is openly available at our GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05320 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The OCON model: an old but gold solution for distributable supervised classification Giacomelli, Stefano Giordano, Marco Rinaldi, Claudia Audio and Speech Processing Artificial Intelligence Computation and Language Databases Machine Learning Sound 68T07, 68T09, 68T10, 68T50, 91F20 I.2.7; I.2.11; I.5.1; I.5.2; I.5.5; J.5; E.4; D.2.7; D.2.13 This paper introduces to a structured application of the One-Class approach and the One-Class-One-Network model for supervised classification tasks, specifically addressing a vowel phonemes classification case study within the Automatic Speech Recognition research field. Through pseudo-Neural Architecture Search and Hyper-Parameters Tuning experiments conducted with an informed grid-search methodology, we achieve classification accuracy comparable to nowadays complex architectures (90.0 - 93.7%). Despite its simplicity, our model prioritizes generalization of language context and distributed applicability, supported by relevant statistical and performance metrics. The experiments code is openly available at our GitHub. |
| title | The OCON model: an old but gold solution for distributable supervised classification |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language Databases Machine Learning Sound 68T07, 68T09, 68T10, 68T50, 91F20 I.2.7; I.2.11; I.5.1; I.5.2; I.5.5; J.5; E.4; D.2.7; D.2.13 |
| url | https://arxiv.org/abs/2410.05320 |