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Explaining Conformer-1’s underperformance on the Numbers domain

Determine the factors responsible for Conformer-1’s inferior performance relative to other providers on the in-house Numbers domain and ascertain whether the use of pseudo-labels and the applied filtering strategy causally contribute to this discrepancy.

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Background

In head-to-head comparisons against commercial ASR providers on multiple in-house domains, Conformer-1 wins most domains but lags on the Numbers domain. The authors hypothesize that pseudo-labeling choices and filtering strategies may be affecting numerical transcription quality.

They explicitly state that understanding this discrepancy remains unresolved and identify it as an area for future research, indicating the need for targeted analysis of dataset composition, labeling strategies, and model behavior on numerical content.

References

Conformer-1 outperforms other providers on all domains except Numbers: we hypothesize that the difference between Conformer-1 and other models in this domain can be attributed to the use of pseudo-labels and our filtering strategy. We leave this discrepancy open as a future area of research.

Conformer-1: Robust ASR via Large-Scale Semisupervised Bootstrapping (2404.07341 - Zhang et al., 10 Apr 2024) in Subsection 'Comparison with other Speech APIs' in the Experiments section