Paper deals with the recognition of disease codes and with the hybrid recognition technology. It is impossible so far to recognize about 15000 various diseases, but each disease can be identified by its code, consisting of one letter and some digits. The appropriate Lithuanian names were selected for each letter and their recognition accuracy together with Lithuanian digit names recognition accuracy was investigated. By the hybrid approach we assume the combination of two different recognizers to achieve higher recognition accuracy. The first recognizer was HTK-based Lithuanian recognizer, the second one – the Spanish language recognizer adapted to the Lithuanian language. The experimental results show that a hybrid decision-making rule learned by “random forest” classifier decreases the recognition error of Lithuanian digits names speech corpus by 74.1% and the recognition error of Lithuanian names speech corpus by 76.7% compared with HTK-based Lithuanian recognizer when the speaker is unknown.
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