Dynamic vocabulary prediction for isolated-word dictation on embedded devices

Large-vocabulary speech recognition systems have mainly been developed for fast processors and large amounts of memory that are available on desktop computers and network servers. Much progress has been made towards running these systems on portable devices. Challenges still exist, however, when developing highly efficient algorithms for real-time speech recognition on resource-limited embedded platforms. In this paper, a dynamic vocabulary prediction approach is proposed to decrease the memory footprint of the speech recognizer decoder by keeping the decoder vocabulary small. This leads to reduced acoustic confusion as well as achieving very efficient use of computational resources. Experiments on an isolated-word SMS dictation task have shown that 40% of the vocabulary prediction errors can be eliminated compared to the baseline system.