Exploring rhythm formant analysis for Indic language classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917797384159232 |
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| author | Gogoi, Parismita Kalita, Sishir Sarmah, Priyankoo Prasanna, S. R Mahadeva |
| author_facet | Gogoi, Parismita Kalita, Sishir Sarmah, Priyankoo Prasanna, S. R Mahadeva |
| contents | This paper reports a preliminary study on quantitative frequency domain rhythm cues for classifying five Indian languages: Bengali, Kannada, Malayalam, Marathi, and Tamil. We employ rhythm formant (R-formants) analysis, a technique introduced by Gibbon that utilizes low-frequency spectral analysis of amplitude modulation and frequency modulation envelopes to characterize speech rhythm. Various measures are computed from the LF spectrum, including R-formants, discrete cosine transform-based measures, and spectral measures. Results show that threshold-based and spectral features outperform directly computed R-formants. Temporal pattern of rhythm derived from LF spectrograms provides better language-discriminating cues. Combining all derived features we achieve an accuracy of 69.21% and a weighted F1 score of 69.18% in classifying the five languages. This study demonstrates the potential of RFA in characterizing speech rhythm for Indian language classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05724 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring rhythm formant analysis for Indic language classification Gogoi, Parismita Kalita, Sishir Sarmah, Priyankoo Prasanna, S. R Mahadeva Audio and Speech Processing Signal Processing I.2.7 This paper reports a preliminary study on quantitative frequency domain rhythm cues for classifying five Indian languages: Bengali, Kannada, Malayalam, Marathi, and Tamil. We employ rhythm formant (R-formants) analysis, a technique introduced by Gibbon that utilizes low-frequency spectral analysis of amplitude modulation and frequency modulation envelopes to characterize speech rhythm. Various measures are computed from the LF spectrum, including R-formants, discrete cosine transform-based measures, and spectral measures. Results show that threshold-based and spectral features outperform directly computed R-formants. Temporal pattern of rhythm derived from LF spectrograms provides better language-discriminating cues. Combining all derived features we achieve an accuracy of 69.21% and a weighted F1 score of 69.18% in classifying the five languages. This study demonstrates the potential of RFA in characterizing speech rhythm for Indian language classification. |
| title | Exploring rhythm formant analysis for Indic language classification |
| topic | Audio and Speech Processing Signal Processing I.2.7 |
| url | https://arxiv.org/abs/2410.05724 |