Transformer-Based Approach for Solving Mathematical Problems Using Automatic Speech Recognition
In this paper, we introduce Vox Calculi, a system designed to solve mathematical problems using voice transcriptions. By leveraging state-of-the-art pretrained Automatic Speech Recognition (ASR) models, we accurately transcribe users’ voice recordings. Additionally, we develop a specializ...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10975750/ |
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| Summary: | In this paper, we introduce Vox Calculi, a system designed to solve mathematical problems using voice transcriptions. By leveraging state-of-the-art pretrained Automatic Speech Recognition (ASR) models, we accurately transcribe users’ voice recordings. Additionally, we develop a specialized mathematical parser that converts natural language mathematical expressions into symbolic representations and numerical values while preserving the remainder of the transcription. We utilize Transformer and TP-Transformer models, trained on DeepMind’s Mathematics dataset, to generate an appropriate mathematical answer from the provided input sequence. An in-depth evaluation of both the ASR and Transformer models demonstrates highly satisfactory results. Furthermore, we propose potential future improvements to enhance the system’s performance. |
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| ISSN: | 2169-3536 |