PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909309395271680 |
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| author | Baali, Massa Aldoobi, Abdulhamid Dhamyal, Hira Singh, Rita Raj, Bhiksha |
| author_facet | Baali, Massa Aldoobi, Abdulhamid Dhamyal, Hira Singh, Rita Raj, Bhiksha |
| contents | Speaker verification systems are crucial for authenticating identity through voice. Traditionally, these systems focus on comparing feature vectors, overlooking the speech's content. However, this paper challenges this by highlighting the importance of phonetic dominance, a measure of the frequency or duration of phonemes, as a crucial cue in speaker verification. A novel Phoneme Debiasing Attention Framework (PDAF) is introduced, integrating with existing attention frameworks to mitigate biases caused by phonetic dominance. PDAF adjusts the weighting for each phoneme and influences feature extraction, allowing for a more nuanced analysis of speech. This approach paves the way for more accurate and reliable identity authentication through voice. Furthermore, by employing various weighting strategies, we evaluate the influence of phonetic features on the efficacy of the speaker verification system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05799 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification Baali, Massa Aldoobi, Abdulhamid Dhamyal, Hira Singh, Rita Raj, Bhiksha Sound Computation and Language Speaker verification systems are crucial for authenticating identity through voice. Traditionally, these systems focus on comparing feature vectors, overlooking the speech's content. However, this paper challenges this by highlighting the importance of phonetic dominance, a measure of the frequency or duration of phonemes, as a crucial cue in speaker verification. A novel Phoneme Debiasing Attention Framework (PDAF) is introduced, integrating with existing attention frameworks to mitigate biases caused by phonetic dominance. PDAF adjusts the weighting for each phoneme and influences feature extraction, allowing for a more nuanced analysis of speech. This approach paves the way for more accurate and reliable identity authentication through voice. Furthermore, by employing various weighting strategies, we evaluate the influence of phonetic features on the efficacy of the speaker verification system. |
| title | PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification |
| topic | Sound Computation and Language |
| url | https://arxiv.org/abs/2409.05799 |