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README.md
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@@ -43,7 +43,7 @@ We evaluated granite-speech-4.1-2b alongside other speech-language models in the
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We evaluated the model’s keyword list biasing (KWB) capability by comparing performance with and without KWB applied at inference time.
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We report the F1 scores of transcribed keywords during ASR tasks, excluding common words from the evaluation.
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. PER (punctuation error rate) measures errors in the insertion, deletion, or substitution of punctuation marks (periods, commas, and question marks). Cap-F1 (capitalization F1) measures how accurately the model capitalizes relevant words in the output. Note that our Cap-F1 is computed on Levenshtein-aligned matching word pairs rather than fully matching sentences, allowing evaluation even in the presence of ASR errors.
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We evaluated the model’s keyword list biasing (KWB) capability by comparing performance with and without KWB applied at inference time.
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We report the F1 scores of transcribed keywords during ASR tasks, excluding common words from the evaluation.
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We also evaluated our model on a variety of corpora to assess its punctuation and capitalization capabilities. We report the metrics as defined in [LibriSpeech-PC](https://arxiv.org/abs/2310.02943). PER (punctuation error rate) measures errors in the insertion, deletion, or substitution of punctuation marks (periods, commas, and question marks). Cap-F1 (capitalization F1) measures how accurately the model capitalizes relevant words in the output. Note that our Cap-F1 is computed on Levenshtein-aligned matching word pairs rather than fully matching sentences, allowing evaluation even in the presence of ASR errors.
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