Sota Hikasa | Searcharxiv

arXiv2026

Spoken Digit Recognition and Speaker Classification by Nonlinear Interfered Spin Wave-Based Physical Reservoir Computing

Recently, artificial-intelligence (AI) technologies have been increasingly utilized in a wide range of real-world applications. Speech recognition is one of these practical AI tasks and is regarded as a key application for edge AI systems. Consequently, speech recognition has been widely employed as a representative benchmark task for assessing the performance of physical reservoir computing (PRC). Although many PRCs have performed this task, the majority of them rely on the frequency-extraction preprocessing method, such as a cochleagram and mel-frequency cepstrum. Especially about the cochleagram, this method enables high-accuracy recognition; however, it requires a substantial computational cost for preprocessing and is unsuitable for edge computing, due to the limited resources. In this study, we employed a nonlinear interfered spin wave-based PRC, which demonstrated superior computational performance in mathematical tasks. Using this PRC, we evaluated the performance for two types of speech recognition, spoken digit recognition and speaker classification under four configurations: cochleagram-alone, interfered spin wave-based PRC with cochleagram, baseline without PRC, and interfered spin wave-based PRC alone to quantify the contributions of the cochleagram and of the interfered spin wave-based PRC for each task. As a result, although the cochleagram alone yielded accuracies around 90 % for both tasks, the accuracy reached 85.8 % for speaker classification when only the interfered spin wave-based PRC was used. These results indicate the potential of the proposed PRC to handle speech recognition tasks without cochleagram preprocessing.

physics.app-ph↗

arXiv2026

Spectral dynamics reservoir computing for high-speed hardware-efficient neuromorphic processing

Physical reservoir computing (PRC) is a promising brain-inspired computing architecture for overcoming the von Neumann bottleneck by utilizing the intrinsic dynamics of physical systems. However, a major obstacle to its real-world implementation lies in the tension between extracting sufficient information for high computational performance and maintaining a hardware-feasible, high-speed architecture. Here, we report spectral dynamics reservoir computing (SDRC), a broadly applicable framework based on analogue filtering and envelope detection that bridges this gap. SDRC effectively exploits the fast spectral dynamics embedded in short-time, coarse spectra of material responses to attain strong computational capability while maintaining high-speed processing and minimal hardware overhead. This approach circumvents the need for implementation-intensive, precision-sensitive integrated circuits required in high-speed time-multiplexing measurements, while enabling real-time use of the material's spectral manifold as a high-dimensional computational resource. We implement and experimentally demonstrate SDRC applied to spin waves that achieves state-of-the-art-level performance with only 56 nodes on benchmark tasks of parity-check and second-order nonlinear autoregressive moving average, as well as high accuracy of 98.0% on a real-world problem of speech recognition.

cs.ET↗