Investigating the Zero-Shot Audio Classification Ability of Automatic Speech Recognition Foundation Models

Core Concepts

ASR foundation models like Whisper and MMS, trained primarily for speech recognition, can be leveraged for zero-shot audio classification tasks without any further training or parameter updates.

Abstract

This paper investigates the emergent zero-shot audio classification abilities of large-scale Automatic Speech Recognition (ASR) foundation models, which were not explicitly trained for these downstream tasks.
The key highlights and insights are:

Using simple template-based text prompts, the Whisper ASR model can achieve promising zero-shot classification performance on a range of 8 audio classification datasets, outperforming existing state-of-the-art zero-shot baselines by an average of 9%.

An important step to unlock this emergent ability is debiasing the model outputs. A simple unsupervised reweighting method (prior matching) yields consistent and significant performance gains, improving the average accuracy from 30% to 48.2%.

Performance increases with model size, implying that as ASR foundation models scale up, they may exhibit improved zero-shot audio classification abilities.

The paper also provides a preliminary investigation of Whisper's zero-shot audio question answering capabilities, demonstrating that it can answer yes/no questions about audio inputs with performance significantly better than random.

Overall, this work shows that ASR foundation models can be effectively leveraged for zero-shot audio classification, without any further training or parameter updates, by using simple prompting techniques and calibration methods.

Stats

The Whisper large-v2 model achieves an average zero-shot accuracy of 30% across 8 audio classification datasets, compared to a random performance of 10.4%.
Using prior matching to debias the model outputs, the average accuracy improves to 48.2%.
On the Clotho-AQA audio question answering dataset, the zero-shot Whisper model achieves an accuracy of 64.0% on the unanimous test set, significantly better than random performance.

Quotes

"Whisper shows promising zero-shot classification performance on a range of 8 audio-classification datasets, outperforming the accuracy of existing state-of-the-art zero-shot baselines by an average of 9%."
"One important step to unlock the emergent ability is debiasing, where a simple unsupervised reweighting method of the class probabilities yields consistent significant performance gains."
"Performance increases with model size, implying that as ASR foundation models scale up, they may exhibit improved zero-shot performance."

Key Insights Distilled From

Investigating the Emergent Audio Classification Ability of ASR Foundation Models

by Rao Ma,Adian... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2311.09363.pdf

Deeper Inquiries

How can the zero-shot audio classification capabilities of ASR models be further improved, beyond the debiasing techniques explored in this work?

To further enhance the zero-shot audio classification capabilities of ASR models, several strategies can be considered:

Data Augmentation: Increasing the diversity and quantity of training data can help the model generalize better to unseen tasks. Augmentation techniques such as pitch shifting, time stretching, and adding background noise can expose the model to a wider range of audio variations.

Transfer Learning: Pre-training the ASR models on a more diverse set of audio data or related tasks can provide a stronger foundation for zero-shot classification. Fine-tuning the model on a broader range of tasks before zero-shot evaluation can improve its adaptability.

Multi-Task Learning: Training the ASR model on multiple related tasks simultaneously can help it learn more robust and generalizable representations. By incorporating various audio classification tasks during training, the model can develop a better understanding of different audio features.

Attention Mechanisms: Enhancing the attention mechanisms within the ASR model can help it focus on relevant audio features during classification. Attention mechanisms can be optimized to capture important audio cues for different tasks, improving the model's performance in zero-shot scenarios.

Ensemble Methods: Combining predictions from multiple ASR models or different architectures can lead to more robust and accurate classifications. Ensemble methods can help mitigate individual model biases and errors, improving overall performance in zero-shot settings.

By incorporating these strategies along with debiasing techniques, the zero-shot audio classification capabilities of ASR models can be further improved, enabling them to excel in a wider range of tasks and datasets.

What are the potential limitations or failure modes of using ASR models for zero-shot audio classification, and how can these be addressed?

While ASR models show promise in zero-shot audio classification, there are several limitations and failure modes to consider:

Class Imbalance: ASR models may struggle with imbalanced datasets, leading to biased predictions towards majority classes. Addressing this issue requires careful data preprocessing, augmentation, or calibration techniques to ensure fair representation of all classes.

Domain Shift: ASR models trained on specific datasets may not generalize well to diverse or unseen audio domains. Domain adaptation techniques can help the model adapt to new data distributions and improve performance in zero-shot scenarios.

Ambiguity in Audio: Audio signals can be inherently ambiguous, making it challenging for ASR models to accurately classify certain sounds or emotions. Providing additional context or incorporating multimodal information can help disambiguate audio inputs and enhance classification accuracy.

Limited Vocabulary: ASR models may struggle with audio samples containing rare or out-of-vocabulary words or sounds. Expanding the model's vocabulary through continual learning or dynamic adaptation can mitigate this limitation.

Complex Audio Tasks: Some audio tasks, such as audio-based reasoning or multimodal understanding, may require deeper semantic understanding beyond simple classification. Enhancing the model's architecture with reasoning modules or attention mechanisms tailored to these tasks can address this limitation.

By addressing these limitations through data preprocessing, model enhancements, and domain adaptation techniques, ASR models can overcome challenges in zero-shot audio classification and improve their overall performance and robustness.

Given the promising results on audio question answering, how can ASR foundation models be leveraged for other audio-centric tasks beyond classification, such as audio-based reasoning or multimodal understanding?

To leverage ASR foundation models for advanced audio-centric tasks beyond classification, such as audio-based reasoning or multimodal understanding, the following approaches can be considered:

Semantic Understanding: Enhance the ASR models with semantic parsing capabilities to extract meaning and context from audio inputs. This can enable the model to perform reasoning tasks based on the audio content and generate more insightful responses.

Multimodal Fusion: Integrate audio features with other modalities such as text or images to enable multimodal understanding. By combining information from different sources, the model can gain a more comprehensive understanding of the content and perform tasks that require cross-modal reasoning.

Knowledge Graph Integration: Develop knowledge graphs or structured representations of audio data to facilitate reasoning and inference tasks. ASR models can be trained to populate and query these knowledge graphs, enabling them to perform complex reasoning tasks based on audio inputs.

Attention Mechanisms: Enhance the model's attention mechanisms to focus on relevant audio segments during reasoning or multimodal tasks. Adaptive attention mechanisms can help the model dynamically adjust its focus based on the task requirements, improving performance in complex scenarios.

Continual Learning: Implement continual learning strategies to enable ASR models to adapt and learn from new audio-centric tasks over time. By continuously updating the model with new data and feedback, it can improve its performance on a wide range of tasks and maintain relevance in evolving audio environments.

By incorporating these strategies and advancing the capabilities of ASR foundation models, they can be effectively leveraged for a variety of advanced audio-centric tasks, including audio-based reasoning, multimodal understanding, and more complex forms of audio processing and analysis.