arXiv:2606.24169v1 Announce Type: new Abstract: Adapting a streaming speech recognition model to a new language requires choosing between two plausible warm starts: a multilingual (ML) encoder or an English-only (EN) encoder. The common intuition is that the multilingual encoder should help most at low data, but it is unclear how long that advantage persists, whether tight streaming latency amplifies it, and whether it survives deployment quantization. We answer these questions with a controlled sweep of a 0.6 B-parameter cache-aware FastConformer transducer across eight European languages, up
The paper provides timely insights into optimal model adaptation strategies amidst the rapid global expansion and multilingual requirements of AI-powered speech systems.
This research clarifies critical architectural and data considerations for deploying robust, low-latency multilingual AI, directly influencing the efficiency and cost of global AI services.
The understanding that data scale, rather than just latency, is the primary factor influencing cross-lingual encoder transfer in streaming ASR, challenging common assumptions in model development.
- · AI model developers
- · Cloud AI providers
- · Companies operating in diverse linguistic markets
- · Researchers optimizing multilingual AI
- · Developers neglecting data efficiency in multilingual models
- · Systems with suboptimal language adaptation
More efficient development and deployment of streaming Automatic Speech Recognition (ASR) across numerous languages.
Reduced operational costs and improved performance for global voice-enabled applications and services.
Accelerated adoption of AI in non-English speaking markets due to more effective and localized solutions.
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Read at arXiv cs.AI