Modern ASR models with noisy training and language models correlate better with human WER for speech enhancement evaluation than simpler models, yet their robustness makes them less suitable for purely acoustic assessments.
Too Good to Be True: A Study on Modern Automatic Speech Recognition for the Evaluation of Speech Enhancement
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abstract
Speech enhancement (SE) systems are typically evaluated using a variety of instrumental metrics. The use of automatic speech recognition (ASR) systems to evaluate SE performance is common in literature, usually in terms of word error rate (WER). However, WER scores depend heavily on the choice of ASR system and text normalization pipeline. In this paper, we investigate how modern ASR models correlate with human recognition of enhanced speech. A listening experiment reveals that modern ASR models with large-scale noisy training and embedded language models correlate more with human WER than simpler ones, with a transducer model providing the most reliable transcriptions. Nevertheless, we also show that these models' robustness to noise and use of context can be uninformative to an acoustics-focused evaluation of enhancement performance.
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Too Good to Be True: A Study on Modern Automatic Speech Recognition for the Evaluation of Speech Enhancement
Modern ASR models with noisy training and language models correlate better with human WER for speech enhancement evaluation than simpler models, yet their robustness makes them less suitable for purely acoustic assessments.