# Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition

## TL;DR

This paper introduces a semi-supervised training method for end-to-end speech recognition that uses dropout to generate multiple hypotheses, improving model reliability and reducing errors.

## Contribution

It proposes a novel dropout-based approach to better capture uncertainty in semi-supervised speech recognition training.

## Key findings

- Significant reduction in ASR errors on TEDLIUM and Adobe datasets.
- Dropout-based hypothesis diversity improves model robustness.
- Enhanced semi-supervised training effectiveness over baseline models.

## Abstract

In this paper, we explore various approaches for semi supervised learning in an end to end automatic speech recognition (ASR) framework. The first step in our approach involves training a seed model on the limited amount of labelled data. Additional unlabelled speech data is employed through a data selection mechanism to obtain the best hypothesized output, further used to retrain the seed model. However, uncertainties of the model may not be well captured with a single hypothesis. As opposed to this technique, we apply a dropout mechanism to capture the uncertainty by obtaining multiple hypothesized text transcripts of an speech recording. We assume that the diversity of automatically generated transcripts for an utterance will implicitly increase the reliability of the model. Finally, the data selection process is also applied on these hypothesized transcripts to reduce the uncertainty. Experiments on freely available TEDLIUM corpus and proprietary Adobe's internal dataset show that the proposed approach significantly reduces ASR errors, compared to the baseline model.