Which AI Model Wins in Telecom Talk? Comparing Speech-to-Text Systems for Turkish Telecom

Overview

Explore cutting-edge research comparing Wav2Vec2, Whisper, TDNN, and LSTM speech-to-text models for Turkish telecom applications, revealing which performs best with technical jargon.

Syllabus

• Course Introduction
Overview of the course objectives
Importance of speech-to-text systems in telecom
• Introduction to AI Models in Speech-to-Text
Overview of Wav2Vec2, Whisper, TDNN, and LSTM
Comparative analysis methodologies
• The Turkish Language in Speech-to-Text Applications
Unique challenges of the Turkish language
Handling technical jargon in telecom
• Wav2Vec2 for Speech-to-Text
Architecture and mechanics
Strengths and limitations in Turkish applications
• Whisper ASR System
Detailed analysis and architecture
Comparative performance in telecom jargon
• TDNN in Speech-to-Text Conversion
Technical insights and design
Efficacy with technical terms
• LSTM Models for Speech Recognition
Understanding LSTM architecture
Performance and adaptability in Turkish
• Comparative Analysis of Models
Benchmarking methodologies
Discussion on performance metrics
• Dataset and Preprocessing
Overview of datasets used
Preprocessing techniques for telecom
• Technical Jargon and Model Performance
How each model handles industry-specific jargon
Case studies and real-world applications
• Evaluation and Metrics
Tools for evaluating speech-to-text accuracy
Metrics specific to Turkish telecom speech
• Conclusion and Future Directions
Summary of findings
Potential future improvements in models
• Practical Session
Hands-on exercises with model implementation
Real-time testing and error analysis
• Final Project
Comparative project involving the models
Presentation and discussion of findings
• Additional Resources and Readings
Suggested articles and papers
Online resources for further learning

Subjects

Computer Science