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Audio AI Researcher & Developer
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Module 7
Self-Supervised Speech Representation
1
Self-Supervised Pretraining for Speech: Why Unlabeled Data Matters
Explain the motivation for self-supervised pretraining in speech processing to leverage unlabeled data.
2
Wav2Vec 2.0 Architecture: Encoder and Transformer
Describe the wav2vec 2.0 architecture, detailing its CNN feature encoder and Transformer context network.
3
Wav2Vec 2.0 Pretraining: Quantization & Contrastive Loss
Explain the wav2vec 2.0 pretraining objective, including the role of the quantization module and contrastive loss.
4
Understanding HuBERT: Architecture and Pretraining
Describe the HuBERT architecture and its offline clustering-based pretraining objective (masked prediction of acoustic units).
5
Fine-tuning Wav2Vec 2.0 for ASR
Fine-tune a pretrained wav2vec 2.0 model for ASR and compare its performance to a model trained from scratch.
6
Cross-Lingual Transfer with Self-Supervised Speech Models
Explain how self-supervised speech models enable transfer learning across different languages and downstream tasks.
7
Analyzing Self-Supervised Speech Representations
Explore and analyze the learned representations from a self-supervised speech model.
Previous module
Supervised Speech Recognition Models
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Text-to-Speech Architectures