Multilingual and Fully Non-Autoregressive ASR with Large Language Model
  Fusion: A Comprehensive Study
W. Ronny Huang Cyril Allauzen Tongzhou Chen Kilol Gupta Ke Hu James Qin Yu Zhang Yongqiang Wang Shuo-Yiin Chang Tara N. Sainath
In the era of large models, the autoregressive nature of decoding often
results in latency serving as a significant bottleneck. We propose a
non-autoregressive LM-fused ASR system that effectively leverages the
parallelization capabilities of accelerator hardware. Our approach combines the
Universal Speech Model (USM) and the PaLM 2 language model in per-segment
scoring mode, achieving an average relative WER improvement across all
languages of 10.8% on FLEURS and 3.6% on YouTube captioning. Furthermore, our
comprehensive ablation study analyzes key parameters such as LLM size, context
length, vocabulary size, fusion methodology. For instance, we explore the
impact of LLM size ranging from 128M to 340B parameters on ASR performance.
This study provides valuable insights into the factors influencing the
effectiveness of practical large-scale LM-fused speech recognition systems.