{'fraction_non_alphanumeric': 0.020642201834862386, 'fraction_numerical': 0.0034403669724770644, 'mean_word_length': 5.235714285714286, '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}}
Rapid adaptation for mobile speech applications

We investigate the use of iVector-based rapid adaptation for recognition in mobile speech applications. We show that on this task, the proposed approach has two merits over a linear-transform based approach. First it provides larger error reductions (11% vs. 6%) as it is better suited for the short utterances and varied recording conditions. Second it omits the need for speaker data pooling and/or clustering and the very large infrastructure complexity that accompanies that. Empirical results show that although the proposed utterance-based training algorithm leads to large data fragmentation, the resulting model re-estimation performs well. Our implementation within the MapReduce framework allows processing of the large statistics that this approach gives rise to when applied on a database of thousands of hours.