A method for keyword recognition in voice signals in resource-constrained computer systems

Keyword spotting on embedded platforms must balance accuracy and strict resource limits while remaining independent of network connectivity. The aim of the study was to develop and experimentally validate a classical, frugal recognition method that increases feature informativeness without increasing model complexity and is suitable for autonomous use on edge devices that rely only on a central processing unit. A weighted acoustic f ingerprinting mechanism was proposed. Mel-frequency cepstral coefficients, together with their derivatives, were reweighted, aggregated, and serialised into compact discrete “fingerprints”, which were then classified using the Levenshtein edit distance. Experiments were carried out on a Ukrainian-language command corpus from six native speakers (three male, three female), recorded with both headsets and far-field microphones; lexicons of 10, 100, and 200 words were evaluated under speaker-independent splits of 70%/15%/15%. The methodology comprised f ixed parametrisation of mel-frequency cepstral coefficients, construction of a static weighting vector, voice activity detection with spectral subtraction, uniform quantisation and serialisation, and deterministic edit-distance classification; for comparison, equal-weight baselines, hidden Markov models with Gaussian mixture emissions, Dynamic Time Warping, a lightweight convolutional neural network, and a reference depthwise-separable convolutional neural network were considered. The proposed method achieved macro-averaged harmonic means of precision and recall of 0.96/0.92/0.89 for 10/100/200-word lexicons in clean audio, and 0.78 at a signal-to-noise ratio of 5 decibels (100-word lexicon). The implementation required approximately 250 kilobytes of memory and operated with a real-time factor of 0.005 on Raspberry Pi 4 with 4 gigabytes, i.e., faster than real time. Superiority over equal-weight baselines, hidden Markov models with Gaussian mixture emissions, and Dynamic Time Warping was demonstrated, with performance approaching that of a compact convolutional neural network. It is concluded that weighted acoustic fingerprinting provides a robust, efficient, and autonomous keyword-spotting solution for deployments that use only a central processing unit