Ensembles of Hybrid and End-to-End Speech Recognition.

Abstract

We propose a method to combine the hybrid Kaldi-based Automatic Speech Recognition (ASR) system with the end-to-end wav2vec 2.0 XLS-R ASR using confidence measures. Our research is focused on the low-resource Irish language. Given the limited available open-source resources, neither the standalone hybrid ASR nor the end-to-end ASR system can achieve optimal performance. By applying the Recognizer Output Voting Error Reduction (ROVER) technique, we illustrate how ensemble learning could facilitate mutual error correction between both ASR systems. This paper outlines the strategies for merging the hybrid Kaldi ASR model and the end-to-end XLS-R model with the help of confidence scores. Although contemporary state-of-the-art end-to-end ASR models face challenges related to prediction overconfidence, we utilize Renyi’s entropy-based confidence approach, tuned with temperature scaling, to align it with the Kaldi ASR confidence. Although there was no significant difference in the Word Error Rate (WER) between the hybrid and end-to-end ASR, we could achieve a notable reduction in WER after ensembling through ROVER. This resulted in an almost 14% Word Error Rate Reduction (WERR) on our primary test set and an approximately 20% WERR on other noisy and imbalanced test data.