Word Error Rate Estimation Without ASR Output: e-WER2

• URL: http://arxiv.org/abs/2008.03403v1
• Date: Sat, 8 Aug 2020 00:19:09 GMT
• Title: Word Error Rate Estimation Without ASR Output: e-WER2
• Authors: Ahmed Ali and Steve Renals
• Abstract summary: We use a multistream end-to-end architecture to estimate the word error rate (WER) of speech recognition systems. We report results for systems using internal speech decoder features (glass-box), systems without speech decoder features (black-box) and for systems without access to the ASR system (no-box) Considering WER per sentence, our no-box system achieves 0.56 Pearson correlation with the reference evaluation and 0.24 root mean square error (RMSE) across 1,400 sentences.
• Score: 36.43741370454534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Measuring the performance of automatic speech recognition (ASR) systems requires manually transcribed data in order to compute the word error rate (WER), which is often time-consuming and expensive. In this paper, we continue our effort in estimating WER using acoustic, lexical and phonotactic features. Our novel approach to estimate the WER uses a multistream end-to-end architecture. We report results for systems using internal speech decoder features (glass-box), systems without speech decoder features (black-box), and for systems without having access to the ASR system (no-box). The no-box system learns joint acoustic-lexical representation from phoneme recognition results along with MFCC acoustic features to estimate WER. Considering WER per sentence, our no-box system achieves 0.56 Pearson correlation with the reference evaluation and 0.24 root mean square error (RMSE) across 1,400 sentences. The estimated overall WER by e-WER2 is 30.9% for a three hours test set, while the WER computed using the reference transcriptions was 28.5%.

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