Difference between revisions of "Projects:2018s1-103 Improving Usability and User Interaction with KALDI Open-Source Speech Recogniser"
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This project will involve the use of Deep Learning algorithms (Automatic Speech Recognition related), software development (C++) and performance evaluation through the Word Error Rate formula. Very little hardware will be involved through its entirety. | This project will involve the use of Deep Learning algorithms (Automatic Speech Recognition related), software development (C++) and performance evaluation through the Word Error Rate formula. Very little hardware will be involved through its entirety. | ||
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| + | == '''Introduction''' == | ||
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| + | == '''Background''' == | ||
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| + | == '''Research and Development''' == | ||
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| + | == '''Results''' == | ||
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| + | == '''Conclusion''' == | ||
Revision as of 22:27, 18 October 2018
Contents
Project Team
Students
- Shi Yik Chin
- Yasasa Saman Tennakoon
Supervisors
- Dr. Said Al-Sarawi
- Dr. Ahmad Hashemi-Sakhtsari (DST Group)
Abstract
This project aims to refine and improve the capabilities of KALDI (an Open Source Speech Recogniser). This will require:
- Improving the current GUI's flexibility
- Introducing new elements or replacing older elements in the GUI for ease of use
- Including a methodology that users (of any skill level) can use to improve or introduce Language or Acoustic models into the software
- Refining current Language and Acoustic models in the software to reduce the Word Error Rate (WER)
- Introducing a neural network in the software to reduce the Word Error Rate (WER)
- Introducing a feedback loop into the software to reduce the Word Error Rate (WER)
- Introducing Binarized Neural Networks into the training methods to reduce training times and increase efficiency
This project will involve the use of Deep Learning algorithms (Automatic Speech Recognition related), software development (C++) and performance evaluation through the Word Error Rate formula. Very little hardware will be involved through its entirety.
