Robust Speech Recognition via Large-Scale Weak Supervision
The paper "Robust Speech Recognition via Large-Scale Weak Supervision" explores the development of Whisper, a speech processing system trained on a large and multilingual weakly supervised dataset. The Whisper models, which include up to 680,000 hours of labeled audio data, aim to achieve superior robustness and generalization. These models demonstrate their capability to perform high-quality speech recognition and translate directly from raw transcripts sourced from the internet, without requiring fine-tuning specific to particular datasets.
Key Contributions
- Large-Scale Weak Supervision: The authors argue that current supervised datasets are limited in size and thus propose a significant shift toward leveraging a weakly supervised dataset. By gathering 680,000 hours of audio from multilingual sources, they break the barrier of available training data, extending capabilities beyond English-centric models.
- Unified Multitask and Multilingual Model: Whisper's architecture supports multiple tasks and languages by encoding them with special tokens. This method circumvents the complexity of having separate models for each task, streamlining the process by enabling multitask learning within a single framework.
- Robustness and Zero-Shot Transfer: The paper emphasizes the model's performance without fine-tuning, showing that Whisper maintains competitive results across various benchmarks and datasets. Compared to existing systems, Whisper exhibits enhanced robustness and better adaptability to unseen data distributions.
- Contrast with Fine-Tuned Models: Whisper models, despite their significant size, demonstrate competitive or superior performance compared to smaller, fine-tuned models on miscellaneous, out-of-distribution tasks. The results suggest that the robustness of Whisper approaches human-level generalization better than traditional models fine-tuned on specific datasets.
Experimental Analysis
The authors conduct extensive evaluations on various short-form English-only and multilingual datasets:
- English Speech Recognition: The Whisper models show promising results on benchmarks like LibriSpeech, TED-LIUM3, and Common Voice. Notably, they excel in scenarios where traditional models falter when exposed to out-of-training-distribution examples.
- Multilingual Speech Recognition: The paper covers 15 languages from the MLS and VoxPopuli datasets and further investigates 75 languages in the Fleurs dataset. Whisper achieves significant improvements due to the expanded training corpus.
- Speech Translation: Performance in speech translation is evaluated using CoVoST2, with Whisper outperforming other models in many low-resource languages. This result highlights the effectiveness of Whisper in translating between languages within the scope of its training data.
- Noise Robustness: Whisper's models are also tested against additive noise, showcasing superior performance under noisy conditions compared to models fine-tuned for specific datasets.
Insights on Scaling
The paper details the scaling of the model size and training data:
- Model Scaling: The results indicate continuous improvement in performance across speech recognition, multilingual tasks, and translation as model size increases. Notably, the results saturate for English speech recognition, potentially indicating nearing human-level performance.
- Dataset Scaling: Subsampling experiments reveal a consistent improvement with larger datasets, suggesting that Whisper models have yet to reach the limits of performance scalability.
Future Directions and Implications
The work highlights several paths for future research:
- Improved Decoding Strategies: Addressing model shortcomings such as hallucination and repetition through advanced strategies or reinforcement learning could refine long-form transcription.
- Dataset Diversity Enhancement: Targeted efforts to increase training data for lower-resource languages can further expand Whisper's multilingual capabilities.
- Fine-Tuning Studies: Although this research focuses on zero-shot performance, fine-tuning for specific tasks and datasets might unlock further potential and provide a more comprehensive comparison with existing models.
- Continued Examination of Robustness: Further analysis on the impact of LLMs and auxiliary objectives on robustness can provide clearer insights into the efficacy of Whisper's design choices.
Conclusion
"Robust Speech Recognition via Large-Scale Weak Supervision" presents Whisper as an exemplar of leveraging weakly supervised, large-scale training for developing more robust and generalizable speech recognition systems. By eschewing fine-tuning and focusing on zero-shot performance across a diverse range of audio sources, Whisper sets a new standard for robustness in speech processing, demonstrating significant improvements in both multilinguality and multitask learning.