Speech Enhancement for Electrolarynx Devices Using M-RLS: Intelligibility Improvement and Low-Power Hardware Feasibility

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Abstract
An electrolarynx, a widely used assistive device for voice restoration for individuals with laryngeal impairments, often suffers from mechanical buzz and poor intelligibility, limiting its everyday applicability. This work presents a modified recursive least squares (M-RLS) algorithm specifically designed to enhance electrolarynx speech by attenuating the excitation noise characteristic while preserving speech quality. The proposed work is evaluated on data across 44 speech samples and benchmarked against a baseline method using three objective metrics: the perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and the signal-to-noise ratio (SNR). The proposed M-RLS approach demonstrated notable improvements, with PESQ scores reaching 3.01, a median STOI value of 0.7, and an SNR performance comparable to that of the baseline. In addition, to evaluate its feasibility in embedded systems, the algorithm was synthesized in 15 nm technology to form an application-specific integrated circuit (ASIC), confirming its suitability for real-time, low-power speech enhancement applications. The design occupies 0.289 mm², operates at 1 GHz, and consumes only 16.9 mW, yielding a power-delay product of 15.41 pJ, making it well suited for energy-constrained environments such as electrolarynx-based speech enhancement systems. The proposed method is also quick prototyped via a DE10 field programmable gate array (FPGA) with a maximum operating frequency of 253.8 MHz.

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