Accurate client-server based speech recognition keeping personal data on the client

In this paper, a novel technique is proposed that recognizes speech on a server but all private knowledge is processed on the client. Private knowledge could be address book entries, calendar entries or medical patient data. The technique combines the advantage of a powerful server with almost unlimited memory and the advantage using locally available user dependent knowledge. A dynamic language model is used to recognize speech with the help of content dependent acoustic fillers on a server. The result is then recognized including user dependent knowledge on a client, e.g., a smart phone. We achieved a word error rate reduction of 17% on the Wall Street Journal Corpus.