Word Error Rate Estimation for Speech Recognition: e-WER

Code

• github.com/qcri/e-wer
  OfficialIn papernone★ 16

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 propose a novel approach to estimate WER, or e-WER, which does not require a gold-standard transcription of the test set. Our e-WER framework uses a comprehensive set of features: ASR recognised text, character recognition results to complement recognition output, and internal decoder features. We report results for the two features; black-box and glass-box using unseen 24 Arabic broadcast programs. Our system achieves 16.9\% WER root mean squared error (RMSE) across 1,400 sentences. The estimated overall WER e-WER was 25.3\% for the three hours test set, while the actual WER was 28.5\%.

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