A new trend is emerging in the semiconductor industry that future computation speedups will likely come more from parallelism than from having faster individual computing elements. Most algorithm designers for the current, HMMbased speech recognition systems, which have the recognition performance significantly lower than that of human, have not embraced this trend. This is partly attributed to the state-of-the-art sequential algorithms that have involved extremely clever schemes to speed up singleprocessor performance developed and matured over many years. This invited presentation advances two arguments. First, much more powerful speech systems in the future generations will likely approach human performance with new architectures that integrate rich knowledge sources and overcome the reasonably well understood limitations of the current HMM-based systems. Second, the success of the above endeavor will require complete rethinking of computation issues, likely disposing of the traditional thinking of HMM-centric sequential processing and embracing parallel computing in the new architectures mimicking key aspects of the human speech processing system. Four case studies are provided in this paper extracted from some recent influential work that may shape the foundation of this potentially active research area.
['Li Deng']
Rethinking of computation for future-generation, knowledge-rich speech recognition and understanding