Multi-dialectical Languages Effect On Speech Recognition Too Much Choice Can Hurt
Research has shown that automatic speech recognition (ASR) performance typically decreases when evaluated on a dialectal variation of the same language that was not used for training its models. Similarly, models simultaneously trained on a group of dialects tend to underperform when compared to di...
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| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
Scientific and Technological Research Center for the Development of the Arabic Language
2016-05-01
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| Series: | Al-Lisaniyyat |
| Subjects: | |
| Online Access: | https://www.crstdla.dz/ojs/index.php/allj/article/view/364 |
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| Summary: | Research has shown that automatic speech recognition (ASR) performance typically decreases when evaluated on a dialectal variation of the same language that was not used for training its models. Similarly, models simultaneously trained on a group of dialects tend to underperform when compared to dialect-specific models. When trying to decide which dialect-specific model (recognizer) to use to decode an utterance (e.g., a voice search query), possible strategies include automatically detecting the spoken dialect or following the user's language preferences as set in his/her cell phone. In this paper, we observe that user's voice search queries are usually directed to a dialect-specific recognizer that does not match the user's current location, and present a study that shows that automatically selecting the recognizer based on the user's geographical location helps improve the user experience.
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| ISSN: | 1112-4393 2588-2031 |