Low Power Speech Recognition Signal Selection Circuit

The audio processing system is divided into multiple functional stages: a first stage performs simple analog signal buffering and basic noise reduction with low power consumption, while a second stage performs high-precision analog-to-digital conversion only when needed. This segmentation allows the system to maintain measurement precision for speech recognition while significantly reducing average power consumption by keeping the high-precision converter inactive during most operations.

The system performs preliminary noise reduction and signal buffering using low-power circuitry before the high-power analog-to-digital converter is activated. This preliminary processing prepares the signal for accurate digitisation while minimizing the duration that the high-power components are active, thus resolving the contradiction between precision and energy consumption.

The speech processing algorithm is segmented into multiple stages: an initial low-power stage that performs basic audio buffering and noise reduction, followed by a higher-power stage that performs accurate speech recognition and speaker verification only when the pass phrase is detected. This segmentation enables the system to maintain high measurement precision for speech recognition while reducing average power consumption by keeping complex algorithms inactive during most time periods.

The system uses periodic monitoring at low power to detect pass phrases, then activates complex speech recognition algorithms periodically only when necessary. This periodic activation pattern allows the system to maintain high speech recognition accuracy when needed while minimizing the cumulative energy consumption from running complex algorithms continuously.

The system segments audio processing into a continuously active low-power first stage that performs basic signal buffering and noise reduction, and a second stage that performs hands-free speech recognition only when pass phrases are detected. This segmentation enables hands-free operation during speech recognition while minimizing power consumption by keeping the second stage inactive during most operations.

The system dynamically adjusts its operational state based on detected conditions: it maintains low-power continuous listening for basic audio capture, then transitions to high-power hands-free speech recognition mode only when pass phrases are detected. This dynamic state change allows the system to provide hands-free operation when needed while significantly reducing average power consumption during idle periods.