Single Microphone Own Voice Detection based on Simulated Transfer Functions for Hearing Aids
Abstract
This paper presents a simulation-based approach to own voice detection (OVD) in hearing aids using a single microphone. While OVD can significantly improve user comfort and speech intelligibility, existing solutions often rely on multiple microphones or additional sensors, increasing device complexity and cost. To enable ML-based OVD without requiring costly transfer-function measurements, we propose a data augmentation strategy based on simulated acoustic transfer functions (ATFs) that expose the model to a wide range of spatial propagation conditions. A transformer-based classifier is first trained on analytically generated ATFs and then progressively fine-tuned using numerically simulated ATFs, transitioning from a rigid-sphere model to a detailed head-and-torso representation. This hierarchical adaptation enabled the model to refine its spatial understanding while maintaining generalization. Experimental results show 95.52% accuracy on simulated head-and-torso test data. Under short-duration conditions, the model maintained 90.02% accuracy with one-second utterances. On real hearing aid recordings, the model achieved 80% accuracy without fine-tuning, aided by lightweight test-time feature compensation. This highlights the model's ability to generalize from simulated to real-world conditions, demonstrating practical viability and pointing toward a promising direction for future hearing aid design.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this work is the introduction of a simulation-based framework for single microphone own voice detection in hearing aids, which effectively utilizes simulated acoustic transfer functions to enhance model training and generalization. This innovative approach not only addresses existing challenges in OVD but also sets a promising direction for future advancements in hearing aid technology.
Methodology
The paper proposes a novel approach to own voice detection (OVD) in hearing aids using a single microphone by leveraging simulated acoustic transfer functions (ATFs) for data augmentation. The methodology is well-structured, involving a two-stage simulation-based ATF generation pipeline that transitions from a rigid-sphere model to a detailed head-and-torso representation. The use of a transformer-based classifier enhances the model's ability to learn from spatial propagation cues, which is a significant advancement over traditional methods that rely on multiple microphones or complex signal processing techniques. The hierarchical adaptation strategy employed to progressively fine-tune the model is a commendable aspect, allowing for improved generalization from simulated to real-world conditions.
Results & evaluation
The experimental results demonstrate high accuracy rates, achieving 95.52% on simulated head-and-torso test data and 80% on real hearing aid recordings without fine-tuning. The use of diverse datasets, including VoxCeleb1 and LibriSpeech, alongside real-world recordings, adds robustness to the evaluation. The model's performance under varying noise conditions was also assessed, showcasing its resilience, which is crucial for practical applications in hearing aids.
Reproducibility
While the paper provides a detailed description of the methodology and experimental setup, it lacks specific URLs or repositories for code and data, which would enhance reproducibility. The absence of a demo or project URL limits the ability for other researchers to replicate the findings directly. However, the comprehensive description of the data augmentation process and model training strategies offers a solid foundation for future implementations.
Limitations
One limitation is the reliance on simulated data for training, which may not fully capture the complexities of real-world acoustic environments. The model's performance on real recordings, while promising, may still be affected by factors not accounted for in the simulations. Additionally, the study focuses on offline segment-level detection, leaving out considerations for real-time applications, which are critical in hearing aid technology.
Broader impact
The proposed method has significant implications for the design of hearing aids, particularly in enhancing user comfort and speech intelligibility without increasing device complexity or cost. By enabling effective OVD with a single microphone, this research could lead to more accessible hearing aid solutions for individuals with hearing impairments, potentially improving their quality of life. The main contribution of this work is the introduction of a simulation-based framework for single microphone own voice detection in hearing aids, which effectively utilizes simulated acoustic transfer functions to enhance model training and generalization. This innovative approach not only addresses existing challenges in OVD but also sets a promising direction for future advancements in hearing aid technology.
Author of this paper? Send a correction.