WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm

TL;DR

This paper introduces WER-BERT, a novel BERT-based model for automatic WER estimation that addresses class imbalance and the ordinal nature of WER, achieving state-of-the-art results on standard datasets.

Contribution

It proposes a balanced classification paradigm for e-WER, a new distance loss for ordinal data, and a BERT-based architecture tailored for WER prediction.

Findings

Achieves state-of-the-art accuracy on Librispeech and Google API datasets.

Effectively handles class imbalance in WER classification.

Demonstrates the benefit of the distance loss for ordinal WER estimation.

Abstract

Automatic Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER), which is calculated by comparing the number of errors between the ground truth and the transcription of the ASR system. This calculation, however, requires manual transcription of the speech signal to obtain the ground truth. Since transcribing audio signals is a costly process, Automatic WER Evaluation (e-WER) methods have been developed to automatically predict the WER of a speech system by only relying on the transcription and the speech signal features. While WER is a continuous variable, previous works have shown that positing e-WER as a classification problem is more effective than regression. However, while converting to a classification setting, these approaches suffer from heavy class imbalance. In this paper, we propose a new balanced paradigm for e-WER in a classification setting. Within this paradigm, we also propose WER-BERT, a BERT based architecture with speech features for e-WER. Furthermore, we introduce a distance loss function to tackle the ordinal nature of e-WER classification. The proposed approach and paradigm are evaluated on the Librispeech dataset and a commercial (black box) ASR system, Google Cloud's Speech-to-Text API. The results and experiments demonstrate that WER-BERT establishes a new state-of-the-art in automatic WER estimation.