A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation

Understand

Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to autoregressive baselines.

• Showing great potential for real-time applications, an increasing number of NAR models have been explored in different fields to mitigate the performance gap against AR models.
• In this work, we conduct a comparative study of various NAR modeling methods for end-to-end automatic speech recognition (ASR).
• Experiments are performed in the state-of-the-art setting using ESPnet.