{'fraction_non_alphanumeric': 0.02773246329526917, 'fraction_numerical': 0.0, 'mean_word_length': 5.329896907216495, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
cROVER: Improving ROVER using automatic error detection

Recognizer Output Voting Error Reduction (ROVER), is a well-known procedure for decoders' combination aiming at reducing the Word Error Rate (WER) in transcription applications. However, it appears that this technique has reached a plateau in terms of performance. This paper presents a novel approach, cROVER, in order to boost the current ROVER performance, by relying on a contextual analysis to trim erroneous words. Experiments have proven that it is possible to outperform ROVER, despite the high false positive rate of the error detection technique.