To what extent do traditional ASR metrics like CER fail to predict downstream intent classification performanc

Abstract

Abstract: Sequence-to-sequence models, such as attention-based models in automatic speech recognition (ASR), are typically trained to optimize the cross-entropy criterion which corresponds to improving the log-likelihood of the data. However, system performance is usually measured in terms of word error rate (WER), not log-likelihood. Traditional ASR systems benefit from discriminative sequence training which optimizes criteria such as the state-level minimum Bayes risk (sMBR) which are more closely related to WER. In the present work, we explore techniques to train attention-based models to directly mi

Research Question

To what extent do traditional ASR metrics like CER fail to predict downstream intent classification performance in noisy environments for large-scale speech foundation models?

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