Pulse-Based Arithmetic Unit Uses Time Signals to Significantly Reduce Power Consumption While Enhancing Speech Recognition in Portable Devices
This pulse-based arithmetic unit uses an adaptive network to process auditory signals in digital speech processors that are largely employed in portable electronic devices. Digital speech processing involves the conversion of sound waves to digital signals that can be analyzed by a computing device. Digital speech processing is a widespread technology utilized in such fields as military devices, health equipment, and mobile computing. The global voice and speech recognition market is currently estimated at over $250 million, with a 22% CAGR to 2019, and additional models propose that by 2024 the market is projected to approach $5 billion. Existing speech recognition models, including Hidden Markov Models, prove to be highly complex and thus lack adaptability. Other existing speech recognition systems, such as Apple’s Siri, require cellular or wireless connection to process auditory signals. Researchers at the University of Florida have engineered a speech recognition technology that processes auditory signals at an improved rate while significantly decreasing power usage and size. Additionally, this technology requires no internet connection and can be specifically configured for each individual. This pulse-based digital signal processing technology may be extended to applications including health monitoring and military services.
Pulse-based computation for optimized power consumption and adaptability in digital speech recognition
- Low power pulse-based unit leads to decreased complexity and data rates, improving overall cost-efficiency
- Device decreases both the area and the power consumption of traditional digital signal processors, optimizing processor volume and energy use
- Simple method allows for the configuration of the speech recognizer for each individual, enhancing user adaptability
- Independent processing technology allows for conversion of signals without the presence of internet connection, improving environmental flexibility
This device uses pairs of adjacent pulses coupled with the Kernel Adaptive Autoregressive Moving Average (KAARMA) model to process an auditory signal in a clear and efficient manner. The initial auditory signal is converted into a pulse train that is broken up into a series of fragments. Each conversion precisely occurs due to a predefined correlation between sound frequency and plurality of pulse trains. Each fragment of the auditory signal is then processed by applying its resulting pulse train to a KAARMA network. The digital signal processing system then identifies the spoken words according to the pulse-train / KAARMA network relationship, and responds accordingly.