Voice transformation for throat microphones

Abstract

Systems and methods are provided for transforming audio signals captured by a throat microphone into signals emulating speech recorded with a conventional air-conduction microphone. Throat microphones employ vibration sensors positioned on the neck to capture audio, making them suitable for high-noise environments. However, throat microphone signals lack high-frequency components, reducing intelligibility and degrading automatic speech recognition performance. The techniques provided herein apply signal-processing operations and a lightweight neural network to reconstruct missing spectral details. The input signal is converted to log-Mel spectra and modeled as a smooth average spectrum (SAS) plus a residual component. A neural network predicts a conventional-microphone SAS. A vocoder synthesizes an enhanced audio signal after combining the predicted SAS with the residual component. The approach improves speech intelligibility and ASR accuracy while maintaining low computational complexity, enabling real-time, on-device processing in noisy environments and supporting hands-free communication for applications such as collaborative robotics and augmented reality.

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