[2304.00649] Multilingual Word Error Rate Estimation: e-WER3
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Computer Science > Computation and Language

arXiv:2304.00649 (cs)
[Submitted on 2 Apr 2023]

Title:Multilingual Word Error Rate Estimation: e-WER3

Authors:Shammur Absar Chowdhury, Ahmed Ali
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Abstract:The success of the multilingual automatic speech recognition systems empowered many voice-driven applications. However, measuring the performance of such systems remains a major challenge, due to its dependency on manually transcribed speech data in both mono- and multilingual scenarios. In this paper, we propose a novel multilingual framework -- eWER3 -- jointly trained on acoustic and lexical representation to estimate word error rate. We demonstrate the effectiveness of eWER3 to (i) predict WER without using any internal states from the ASR and (ii) use the multilingual shared latent space to push the performance of the close-related languages. We show our proposed multilingual model outperforms the previous monolingual word error rate estimation method (eWER2) by an absolute 9\% increase in Pearson correlation coefficient (PCC), with better overall estimation between the predicted and reference WER.
Comments: Accepted in ICASSP, Multilingual WER estimation, End-to-End systems, multilingual model, automatic word error rate estimation
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2304.00649 [cs.CL]
  (or arXiv:2304.00649v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2304.00649

Submission history

From: Shammur Absar Chowdhury [view email]
[v1] Sun, 2 Apr 2023 23:08:11 UTC (3,249 KB)
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