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Punctuation and Segmentation are key to readability in Automatic SpeechRecognition (ASR), often evaluated using F1 scores that require high-qualityhuman transcripts and do not reflect readability well. Human evaluation isexpensive, time-consuming, and suffers from large inter-observer variability,especially in conversational speech devoid of strict grammatical structures.Large pre-trained models capture a notion of grammatical structure. We presentTRScore, a novel readability measure using the GPT model to evaluate differentsegmentation and punctuation systems. We validate our approach with humanexperts. Additionally, our approach enables quantitative assessment of textpost-processing techniques such as capitalization, inverse text normalization(ITN), and disfluency on overall readability, which traditional word error rate(WER) and slot error rate (SER) metrics fail to capture. TRScore is stronglycorrelated to traditional F1 and human readability scores, with Pearson'scorrelation coefficients of 0.67 and 0.98, respectively. It also eliminates theneed for human transcriptions for model selection.

TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection