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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.05674 |
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| _version_ | 1866909842626576384 |
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| author | Arriaga, Carlos Pozo, Alejandro Conde, Javier Alonso, Alvaro |
| author_facet | Arriaga, Carlos Pozo, Alejandro Conde, Javier Alonso, Alvaro |
| contents | Automatic speech recognition (ASR) systems generate real-time transcriptions but often miss nuances that human interpreters capture. While ASR is useful in many contexts, interpreters-who already use ASR tools such as Dragon-add critical value, especially in sensitive settings such as diplomatic meetings where subtle language is key. Human interpreters not only perceive these nuances but can adjust in real time, improving accuracy, while ASR handles basic transcription tasks. However, ASR systems introduce a delay that does not align with real-time interpretation needs. The user-perceived latency of ASR systems differs from that of interpretation because it measures the time between speech and transcription delivery. To address this, we propose a new approach to measuring delay in ASR systems and validate if they are usable in live interpretation scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05674 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assessing Latency in ASR Systems: A Methodological Perspective for Real-Time Use Arriaga, Carlos Pozo, Alejandro Conde, Javier Alonso, Alvaro Sound Artificial Intelligence Computation and Language I.2.7 Automatic speech recognition (ASR) systems generate real-time transcriptions but often miss nuances that human interpreters capture. While ASR is useful in many contexts, interpreters-who already use ASR tools such as Dragon-add critical value, especially in sensitive settings such as diplomatic meetings where subtle language is key. Human interpreters not only perceive these nuances but can adjust in real time, improving accuracy, while ASR handles basic transcription tasks. However, ASR systems introduce a delay that does not align with real-time interpretation needs. The user-perceived latency of ASR systems differs from that of interpretation because it measures the time between speech and transcription delivery. To address this, we propose a new approach to measuring delay in ASR systems and validate if they are usable in live interpretation scenarios. |
| title | Assessing Latency in ASR Systems: A Methodological Perspective for Real-Time Use |
| topic | Sound Artificial Intelligence Computation and Language I.2.7 |
| url | https://arxiv.org/abs/2409.05674 |