speech-prompts-adapters
github.com/ga642381/speech-prompts-adapters ↗This Repository surveys the paper focusing on Prompting and Adapters for Speech Processing.
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NavigationNEWSICASSP 2023 Tutorial InformationAdapters and Prompting for Speech ProcessingReprogramming and PromptingParameter Efficient Learning Methods
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me adapters for speech processing resources from speech-prompts-adapters"
Installation instructions →What's inside
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Adapters and Prompting for Speech Processing
- Adaptive multilingual speech recognition with pretrained modelsAdapters for Speech Processing
Ngoc-Quan Pham et al.
- An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation LearningAdapters for Speech Processing
Samuel Kessler et al.
- An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing TasksPrompting for Speech Processing
Kai-Wei Chang et al.
- A Parameter-Efficient Learning Approach to Arabic Dialect Identification with Pre-Trained General-Purpose Speech ModelAdapters for Speech Processing
Srijith Radhakrishnan et al.
- Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognitionAdapters for Speech Processing
Haoyu Tang et al.
- CHAPTER: Exploiting Convolutional Neural Network Adapters for Self-supervised Speech ModelsAdapters for Speech Processing
Zih-Ching Chen et al.
Reprogramming and Prompting
Parameter Efficient Learning Methods
- BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
Elad Ben Zaken et al.
- LoRA: Low-Rank Adaptation of Large Language Models
Edward J. Hu et al.
- Parameter-Efficient Transfer Learning for NLP
Neil Houlsby et al.
- Towards a Unified View of Parameter-Efficient Transfer Learning
Junxian He et al.
NEWS
ICASSP 2023 Tutorial Information
- ICASSP 2023 - Tutorials
Tutorials
- Slides
Showing a sample of 43 resources. View the full list on GitHub →