Comparative analysis of adapted foreign language and native Lithuanian speech recognizers for voice user interface

Paper presents research results obtained when building a speaker independent hybrid speech recognizer. This recognizer will be integrated as a phrase recognizer in a medical-pharmaceutical information system. The hybrid speech recognizer consists of two recognition components: an adapted commercial Microsoft Spanish speech recognizer and a locally developed hidden Markov models based recognizer implementing Lithuanian acoustic models. Efficiency of both recognition components was evaluated on multiple speaker independent speech recognition tasks. The average accuracy of Lithuanian recognizer was higher reaching 0.6% phrase error rate for user requests in medical-pharmaceutical domain. The adapted commercial Spanish speech recognizer showed the ability to improve the accuracy of Lithuanian recognizer in the worst recognition scenarios. These results proved the hypothesis formulated when proposing the basic idea of hybrid recognition approach: recognition errors from different recognizers built using various techniques are not strongly correlated. This fact could be exploited for improved overall speech recognition accuracy