A hybrid autoregressive transducer (HAT) is a variant of neural transducer that models blank and non-blank posterior distributions separately. In this paper, we propose a novel internal acoustic model (IAM) training strategy to enhance HAT-based speech recognition. IAM consists of encoder and joint networks, which are fully shared and jointly trained with HAT. This joint training not only enhances the HAT training efficiency but also encourages IAM and HAT to emit blanks synchronously which skips the more expensive non-blank computation, resulting in more effective blank thresholding for faster decoding. Experiments demonstrate that the relative error reductions of the HAT with IAM compared to the vanilla HAT are statistically significant. Moreover, we introduce dual blank thresholding, which combines both HAT- and IAM-blank thresholding and a compatible decoding algorithm. This results in a 42-75% decoding speed-up with no major performance degradation.

本研究解决了混合自回归转导器（HAT）在语音识别中的效率和精度不足的问题。我们提出了一种新的内部声学模型（IAM）训练策略，以提高HAT的训练效率，并结合双空白阈值技术，从而在不显著降低识别性能的情况下实现42-75%的解码速度提升。

通过内部声学模型训练和双空白阈值提升基于混合自回归转导器的语音识别