Year: 2025
Type: article
Abstract: 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
Authors: Andrii Didus,, Ihor Tereikovskyi
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7125679081
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