Construct and optimize sophisticated Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) systems. This course details advanced modeling techniques, end-to-end architectures, adaptation methods, and implementation strategies for building high-performance speech processing applications.
Prerequisites Strong ML/DL & Python
Level:
Advanced ASR Architectures
Implement and analyze complex end-to-end ASR models like attention-based encoder-decoders and Transducers.
Speaker and Environment Adaptation
Apply techniques to adapt ASR models to different speakers, accents, and acoustic environments.
Advanced TTS Modeling
Construct sophisticated TTS models focusing on naturalness, prosody control, and voice cloning.
Neural Vocoders
Implement and evaluate modern neural vocoders for high-fidelity speech synthesis.
Model Optimization and Deployment
Apply techniques for optimizing ASR/TTS models for speed, size, and efficient deployment.
Evaluation Methodologies
Utilize advanced metrics and methodologies for evaluating the performance of ASR and TTS systems.
© 2026 ApX Machine LearningEngineered with