SeMaScore : a new evaluation metric for automatic speech recognition tasks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929590226649088 |
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| author | Sasindran, Zitha Yelchuri, Harsha Prabhakar, T. V. |
| author_facet | Sasindran, Zitha Yelchuri, Harsha Prabhakar, T. V. |
| contents | In this study, we present SeMaScore, generated using a segment-wise mapping and scoring algorithm that serves as an evaluation metric for automatic speech recognition tasks. SeMaScore leverages both the error rate and a more robust similarity score. We show that our algorithm's score generation improves upon the state-of-the-art BERTScore. Our experimental results show that SeMaScore corresponds well with expert human assessments, signal-to-noise ratio levels, and other natural language metrics. We outperform BERTScore by 41x in metric computation speed. Overall, we demonstrate that SeMaScore serves as a more dependable evaluation metric, particularly in real-world situations involving atypical speech patterns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07506 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SeMaScore : a new evaluation metric for automatic speech recognition tasks Sasindran, Zitha Yelchuri, Harsha Prabhakar, T. V. Audio and Speech Processing Machine Learning Sound In this study, we present SeMaScore, generated using a segment-wise mapping and scoring algorithm that serves as an evaluation metric for automatic speech recognition tasks. SeMaScore leverages both the error rate and a more robust similarity score. We show that our algorithm's score generation improves upon the state-of-the-art BERTScore. Our experimental results show that SeMaScore corresponds well with expert human assessments, signal-to-noise ratio levels, and other natural language metrics. We outperform BERTScore by 41x in metric computation speed. Overall, we demonstrate that SeMaScore serves as a more dependable evaluation metric, particularly in real-world situations involving atypical speech patterns. |
| title | SeMaScore : a new evaluation metric for automatic speech recognition tasks |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2401.07506 |