An Approach of Using the Web as a Live Corpus for Spoken Transliteration Name Access

An Approach of Using the Web as a Live Corpus for Spoken Transliteration Name Access

Recognizing transliteration names is challenging due to their flexible formulation and coverage of a lexicon. This paper employs the Web as a huge-scale corpus. The patterns extracted from the Web are considered as a live dictionary to correct speech recognition errors. In our approach, the plausible character strings recognized by ASR (Automated Speech Recognition) are regarded as query terms and submitted to Google. The top n returned web page summaries are entered into PAT trees. The terms of the highest scores are selected. Total 100 Chinese transliteration names, including 50 person names and 50 location names, are used as test data. In the ideal case, we input the correct syllable sequences, convert them to text strings and test the recovery capability of using Web corpus. The results show that both the recall rate and the MRR (Mean Reciprocal Rank) are 0.94. That is, the correct answers appear in the top 1 position in 94 cases. When a complete transliteration name recognition system is evaluated, the experiments show that ASR model with a recovery mechanism can achieve 3.82% performance increases compared to ASR only model on character level. Besides, the recovery capability improves the average ranks of correct transliteration names from the 18th to the 3rd positions on word level.