Can Whisper Perform Speech-Based In-Context Learning?

Core Concepts

Whisper models demonstrate effective in-context learning abilities for speech recognition, improving performance without gradient descent.

Abstract

Investigates in-context learning abilities of Whisper ASR models. Proposes a novel speech-based in-context learning approach (SICL). Achieves significant WER reductions using SICL on Chinese dialects. Utilizes k-nearest-neighbours for efficient in-context example selection. Validates findings through speaker adaptation and continuous speech recognition tasks.

Stats

"A k-nearest-neighbours-based in-context example selection technique can be applied to further improve the efficiency of SICL, which can increase the average relative WER reduction to 36.4%." "Compared to ICL for text-based LLMs, SICL consistently reduces the word error rate (WER) irrespective of the Whisper model size or the specific dialect to adapt."

Quotes

"No gradient descent is required for test-time language-level adaptation using SICL." "SICL consistently reduces the word error rate (WER) irrespective of the Whisper model size or the specific dialect to adapt."

Key Insights Distilled From

Can Whisper perform speech-based in-context learning?

by Siyin Wang,C... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2309.07081.pdf

Deeper Inquiries

How does SICL compare to traditional methods of ASR adaptation?

Speech-based In-Context Learning (SICL) offers a novel approach to ASR adaptation compared to traditional methods. Traditional methods often rely on finetuning or retraining models with large amounts of labeled data, which can be time-consuming and resource-intensive. In contrast, SICL allows for test-time adaptation without the need for gradient descent or extensive parameter updates. By leveraging in-context examples, SICL enables models like Whisper to adapt quickly and efficiently based on specific dialects or speakers. One key advantage of SICL is its ability to provide significant relative Word Error Rate (WER) reductions with just a small number of labeled speech samples. This efficiency makes it particularly useful in scenarios where obtaining large amounts of annotated data is challenging or impractical. Additionally, by utilizing contextual cues from in-context examples, SICL can enhance the adaptability and performance of ASR models without requiring extensive model retraining.

What are potential drawbacks or limitations of relying on in-context learning for ASR?

While Speech-based In-Context Learning (SICL) offers several advantages for ASR adaptation, there are also potential drawbacks and limitations associated with this approach: Data Dependency: The effectiveness of SICL heavily relies on the quality and relevance of the provided in-context examples. If the selected examples do not adequately represent the target dialect or speaker variation, it may lead to suboptimal adaptations. Generalization: While SICL shows promising results within specific contexts such as Chinese dialect recognition, its generalizability across diverse languages and accents remains an area that requires further exploration. Model Complexity: Implementing SICL may introduce additional complexity to existing ASR systems due to the need for separate encoding and decoding processes for speech inputs and text labels from in-context examples. Resource Intensiveness: Selecting appropriate in-context examples manually can be labor-intensive, especially when dealing with multiple dialects or speakers, potentially limiting scalability. Overfitting: There is a risk that overly relying on context-specific information from limited examples could lead to overfitting issues if not carefully managed during inference.

How might advancements in speech recognition impact other fields beyond technology?

Advancements in speech recognition have far-reaching implications beyond technology alone: Accessibility: Improved speech recognition technologies can enhance accessibility by enabling individuals with disabilities to interact more effectively with digital devices through voice commands. Healthcare: Enhanced speech recognition capabilities can revolutionize medical transcription services by automating documentation processes accurately and efficiently. 3Education:: Speech-to-text tools powered by advanced recognition systems can facilitate language learning programs by providing real-time transcriptions during lectures. 4Customer Service:: Businesses can leverage sophisticated speech recognition solutions for better customer service experiences through automated call routing systems and sentiment analysis. 5Security:: Voice biometrics enabled by cutting-edge speech recognition algorithms offer robust authentication mechanisms that strengthen security protocols across various industries. These advancements underscore how progress in speech recognition technology transcends conventional boundaries into sectors like healthcare, education, customer service,and security—transforming operations,promoting inclusivity,and enhancing user experiences across diverse domains