ArticleMicromachines2025
A Piezoelectric Micromachined Ultrasonic Transducer-Based Bone Conduction Microphone System for Enhancing Speech Recognition Accuracy.
Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Beam Drift Mitigation and Wide-Range Measurement in a Miniaturized Ultrasonic Gas Flowmeter.Micromachines · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
Speech recognition in noisy environments has long posed a challenge. Air conduction microphone (ACM), the devices typically used, are susceptible to environmental noise. In this work, a customized bone conduction microphone (BCM) system based on a piezoelectric micromachined ultrasonic transducer is developed to capture speech through real-time bone conduction (BC), while a commercial ACM is integrated for simultaneous capture of speech through air conduction (AC). The system enables simpler and more robust BC speech capture. The BC speech capture achieves a signal-to-noise amplitude ratio over five times greater than that of AC speech capture in an environment with a noise level of 68 dB. Instead of using only AC-captured speech, both BC- and AC-captured speech are input into a speech enhancement module. The noise-insensitive BC-captured speech serves as a speech reference to adapt the SE backbone of AC-captured speech. The two types of speech are fused, and noise suppression is applied to generate enhanced speech. Compared with the original noisy speech, the enhanced speech achieves a character error rate reduction of over 20%, approaching the speech recognition accuracy of clean speech. The results indicate that this speech enhancement method based on the fusion of BC- and AC-captured speech efficiently integrates the features of both types of speech, thereby improving speech recognition accuracy in noisy environments. This work presents an innovative system designed to efficiently capture BC speech and enhance speech recognition in noisy environments.
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Registered trials
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