Data Scale, Not Latency, Shapes Cross-Lingual Encoder Transfer in Streaming ASR

The paper presents findings on the effectiveness of multilingual versus English-only encoders in adapting streaming speech recognition models for new languages, using a 0.6 B-parameter FastConformer transducer across eight European languages. The study reveals that the advantage of multilingual initialization diminishes with increased target-language data, becoming negligible at 2500 hours, while streaming latency does not significantly impact performance. Additionally, 4-bit weight-only quantization reduces model size by approximately three times with a minimal increase in word error rate, providing practical guidelines for practitioners in low-data scenarios and independent decision-making on latency and quantization.