{'fraction_non_alphanumeric': 0.013717421124828532, 'fraction_numerical': 0.0, 'mean_word_length': 5.083333333333333, '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}}
Ten case studies of the effect of field conditions on speech recognition errors

The paper follows ten speech recognition services as they are deployed in live trials and services in the telephone network. We focus in particular on measured recognition accuracy in the lab setting, during the first field deployment, and through several field iterations. We cite factors that contribute to loss of accuracy when systems are used by real customers and relate how these problems are identified and overcome. We observe that a common pattern of performance emerges. We show that with proper planning, careful data collection and analysis, and adequate time for technology updates, field performance can match that of the laboratory.