Hardware speech recognition for user interfaces in low cost, low power devices

We propose a system architecture for real-time hardware speech recognition on low-cost, power-constrained devices. The system is intended to support real-time speech-based user interfaces as part of an effort to bring information and communication technologies (ICTs) to underdeveloped regions of the world. Our system architecture exploits a shared infrastructure model. The computationally intensive task of speech model training and retraining is performed offline by shared servers, while the actual recognition of speech is conducted on low-cost hand-held devices using custom hardware. The recognizer is extremely flexible and can support multiple languages or dialects with speaker-independent recognition. Dynamic loading of speech models is used for changing language grammar and retraining, while reprogramming is used to support evolution of recognition algorithms. The focus on small sets of words (at one time) reduces the complexity, cost and power consumption. We design the speech decoder, the central component of the recognizer, and we validate it via a prototype FPGA implementation. We then use ASIC synthesis to estimate power and size for the design. Our evaluations demonstrate an order of magnitude improvement in power compared with optimized recognition software running on a low-power embedded general-purpose processor of the same technology and of similar capabilities. The synthesis also estimates the area of the design to be about 2.5mm, showing potential for lower cost. In designing and testing our recognizer we use datasets in both English and Tamil languages.