Speaker- and language-independent speech recognition in mobile communication systems

We investigate the technical challenges that are faced when making a transition from the speaker-dependent to speaker-independent speech recognition technology in mobile communication devices. Due to globalization as well as the international nature of the markets and the future applications, speaker independence implies the development and use of language-independent automatic speech recognition (ASR) to avoid logistic difficulties. We propose an architecture for embedded multilingual speech recognition systems. Multilingual acoustic modeling, automatic language identification, and on-line pronunciation modeling are the key features which enable the creation of truly language- and speaker-independent ASR applications with dynamic vocabularies and sparse implementation resources. Our experimental results confirm the viability of the proposed architecture. While the use of multilingual acoustic models degrades the recognition rates only marginally, a recognition accuracy decrease of approximately 4% is observed due to sub-optimal on-line text-to-phoneme mapping and automatic language identification. This performance loss can nevertheless be compensated by applying acoustic model adaptation techniques