Enhancing Multilingual Speech Recognition with DistilWhisper: Bridging the Performance Gap for Low-Resource Languages

Core Concepts

A novel approach called DistilWhisper that enhances the performance of smaller Whisper models for low-resource languages while retaining the advantages of multitask and multilingual capabilities.

Abstract

This work focuses on improving the performance of smaller versions of the Whisper multilingual speech recognition model, which exhibits a significant performance gap compared to the larger models, especially for low-resource languages.
The key insights and contributions are:

Comprehensive analysis of biases in the Whisper model family, including speaker-related (gender, age) and model-related (resourcefulness, model size) biases. The analysis reveals that model-related biases are amplified by quantization, impacting low-resource languages and smaller models more severely.

Introduction of DistilWhisper, a novel approach that combines two key strategies to bridge the performance gap:

Lightweight modular ASR fine-tuning of whisper-small using language-specific experts
Knowledge distillation from whisper-large-v2 to effectively boost ASR performance while retaining the robustness inherited from the multitask and multilingual pre-training.

Extensive experiments demonstrating that DistilWhisper outperforms standard fine-tuning or LoRA adapters, improving performance for targeted low-resource languages on both in-domain and out-of-domain test sets, while introducing only a negligible parameter overhead at inference.

Analysis of the gating mechanism in DistilWhisper, showing that it effectively learns to route inputs to the appropriate language-specific modules, leading to significant performance gains.

Exploration of the impact of temperature and distillation loss on the knowledge distillation process, providing insights into the trade-offs between model size, performance, and robustness.

Investigation of the scalability of DistilWhisper, showing its effectiveness in handling an increasing number of languages without compromising performance.

Stats

"Whisper exhibits certain speaker-related biases, such as gender and age, which are kept unchanged after applying quantization to the model."
"Biases associated with the model itself (model-related bias), including language resourcefulness and architecture size, are amplified by quantization."
"Low-resource languages are the most adversely affected by quantization."
"Smaller models experience more significant performance degradation compared to larger ones when quantized."

Quotes

"Can we enhance the performance of smaller models for languages where they currently perform poorly, even though the best model performs well?"
"This phenomenon is often referred to as the curse of multilinguality."
"Recent research findings have demonstrated an alternative solution to the curse of multilinguality, involving equipping moderately sized models with language-specific (LS) modules."

Key Insights Distilled From

Efficient Compression of Multitask Multilingual Speech Models

by Thomas Palme... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00966.pdf

Deeper Inquiries

How can the DistilWhisper approach be extended to handle an even broader range of languages and tasks beyond automatic speech recognition?

The DistilWhisper approach can be extended to handle a broader range of languages and tasks by incorporating additional language-specific routing mechanisms and task-specific modules. To expand the model's capabilities beyond automatic speech recognition (ASR), we can introduce modules tailored to other speech-related tasks such as speech translation, speaker identification, emotion recognition, or even non-speech tasks like language modeling or text generation. By incorporating these specialized modules, DistilWhisper can be adapted to perform a diverse set of tasks across multiple languages.
Furthermore, to handle a wider range of languages, the model can be trained on more diverse and extensive multilingual datasets. By including data from underrepresented languages and dialects, the model can learn to generalize better and perform effectively across a broader linguistic spectrum. Additionally, incorporating techniques like data augmentation, transfer learning, and domain adaptation can help improve the model's performance on languages with limited training data.
Overall, by enhancing the language-specific routing mechanisms, incorporating task-specific modules, and training on diverse datasets, DistilWhisper can be extended to handle a broader range of languages and tasks beyond automatic speech recognition.

What are the potential drawbacks or limitations of the language-specific routing mechanism used in DistilWhisper, and how could they be addressed?

One potential drawback of the language-specific routing mechanism in DistilWhisper is the risk of overfitting to specific languages or tasks. If the routing mechanism becomes too specialized for certain languages or tasks, it may lead to a decrease in performance on more general or diverse datasets. To address this limitation, regularization techniques such as dropout, weight decay, or early stopping can be applied to prevent overfitting and promote generalization across languages.
Another limitation could be the complexity and computational overhead introduced by the language-specific routing mechanism. As the number of languages and tasks increases, the model's architecture may become more intricate, leading to higher training and inference costs. To mitigate this, techniques like model pruning, parameter sharing, or efficient architecture design can be employed to streamline the routing mechanism and reduce computational complexity.
Additionally, the language-specific routing mechanism may struggle with languages that have limited training data or linguistic resources. In such cases, the model may not generalize well to underrepresented languages, leading to performance disparities. To address this, techniques like data augmentation, unsupervised pre-training, or meta-learning can be utilized to improve the model's robustness across diverse language settings.
By carefully addressing these potential drawbacks and limitations through regularization, efficient architecture design, and robust training strategies, the language-specific routing mechanism in DistilWhisper can be optimized for improved performance and scalability.

Given the insights on the impact of temperature and distillation loss, how could the knowledge distillation process be further optimized to achieve an even better balance between model size, performance, and robustness?

To further optimize the knowledge distillation process in DistilWhisper and achieve a better balance between model size, performance, and robustness, several strategies can be implemented:

Fine-tuning Hyperparameters: Experimenting with different temperature settings and distillation loss functions can help fine-tune the knowledge distillation process. By adjusting these hyperparameters based on the specific task and dataset, the model can achieve a better balance between compression, performance, and robustness.

Multi-Teacher Distillation: Instead of distilling knowledge from a single large teacher model, incorporating multiple teacher models with diverse expertise can enhance the distillation process. Each teacher can contribute unique knowledge, leading to a more comprehensive and robust student model.

Adaptive Distillation: Implementing adaptive distillation techniques that dynamically adjust the distillation process based on the complexity of the data or the performance of the student model can improve the overall learning process. Adaptive distillation can help prioritize challenging samples or focus on specific areas where the student model requires improvement.

Regularization and Augmentation: Introducing regularization techniques like knowledge distillation with regularization terms or data augmentation during the distillation process can enhance the model's generalization capabilities. By encouraging the student model to learn more robust and diverse representations, it can better adapt to varying tasks and languages.

Ensemble Distillation: Leveraging ensemble distillation methods where the student model learns from an ensemble of teacher models can improve the model's performance and robustness. By aggregating knowledge from multiple sources, the student model can capture a broader range of information and achieve better overall performance.

By implementing these optimization strategies and continuously refining the knowledge distillation process, DistilWhisper can achieve a better balance between model size, performance, and robustness, leading to enhanced efficiency and effectiveness in handling a wide range of tasks and languages.