Language models play a central role in automatic speech recognition (ASR), yet most methods rely on text-only models unaware of ASR error patterns. Recently, large language models (LLMs) have been applied to ASR correction, but introduce latency and hallucination concerns. We revisit ASR error correction with compact seq2seq models, trained on ASR errors from real and synthetic audio. To scale training, we construct synthetic corpora via cascaded TTS and ASR, finding that matching the diversity of realistic error distributions is key. We propose correction-first decoding, where the correction…
The proliferation of LLMs and the recognition of their limitations (latency, hallucination) in specific applications like ASR correction are driving a re-evaluation of specialized models.
Improving ASR accuracy with more efficient, specialized models reduces reliance on slower, more resource-intensive LLMs, impacting user experience and the cost of AI-powered voice interfaces.
The focus is shifting towards compact, specialized models tailored for specific AI tasks, potentially reducing the infrastructural burden and improving real-time performance of voice-enabled applications.
- · Apple
- · Developers of custom compact AI models
- · Cloud computing providers with specialized hardware
- · Users of voice interfaces
- · Companies solely relying on monolithic LLMs for ASR correction
- · General-purpose LLM providers for these specific tasks
More accurate and responsive voice assistants and AI-driven transcription services will become prevalent.
This efficiency gain could reduce the computational cost of integrating voice AI into a broader range of edge devices and applications.
Increased accessibility and reliability of voice interfaces might accelerate their adoption in critical applications like healthcare and highly automated environments.
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Read at Apple Machine Learning Research