Editing Carnegie Mellon's Harpy System (section)

== Future research ==
For Harpy system, the majority of time used by the system (more that 64%)<ref name=":0" /> is for the generation ofthe autocorrelation and linear predictor coefficients. This problem is not unique to Harpy; all speech systems that use this parametric scheme must face this problem. 

The computational complexity of generating autocorrelation and linear prediction coefficients for speech recognition is still a challenge even with modern techniques and hardware. But the raw computational requirements are still very high for real-time low-latency speech recognition with modern neural network models. Hardware and algorithms have improved, providing much higher peak computing capacities, but real-time performance still requires significant optimization and acceleration. It is an active area of research to find faster and less resource-intensive approaches suitable for embedded/edge applications.