WhisperMask: A Noise Suppressive Mask-Type Microphone for Whisper Speech

TL;DR

WhisperMask is a novel mask-type microphone with a large diaphragm and low sensitivity that significantly improves whisper speech recognition accuracy in noisy environments without relying on signal processing.

Contribution

This paper introduces WhisperMask, a microphone design that passively enhances whispered speech and suppresses background noise, outperforming traditional noise reduction methods.

Findings

30% higher recognition accuracy in 80 dB noise

Maintains high performance without denoising

Outperforms traditional microphones in noisy settings

Abstract

Whispering is a common privacy-preserving technique in voice-based interactions, but its effectiveness is limited in noisy environments. In conventional hardware- and software-based noise reduction approaches, isolating whispered speech from ambient noise and other speech sounds remains a challenge. We thus propose WhisperMask, a mask-type microphone featuring a large diaphragm with low sensitivity, making the wearer's voice significantly louder than the background noise. We evaluated WhisperMask using three key metrics: signal-to-noise ratio, quality of recorded voices, and speech recognition rate. Across all metrics, WhisperMask consistently outperformed traditional noise-suppressing microphones and software-based solutions. Notably, WhisperMask showed a 30% higher recognition accuracy for whispered speech recorded in an environment with 80 dB background noise compared with the pin microphone and earbuds. Furthermore, while a denoiser decreased the whispered speech recognition rate of these two microphones by approximately 20% at 30-60 dB noise, WhisperMask maintained a high performance even without denoising, surpassing the other microphones' performances by a significant margin.WhisperMask's design renders the wearer's voice as the dominant input and effectively suppresses background noise without relying on signal processing. This device allows for reliable voice interactions, such as phone calls and voice commands, in a wide range of noisy real-world scenarios while preserving user privacy.