Speech Separation for Hearing-Impaired Children in the Classroom

TL;DR

This paper develops a real-time, multi-channel speech separation model tailored for hearing-impaired children in noisy classrooms, demonstrating effective adaptation and robustness through targeted training and finetuning strategies.

Contribution

It introduces a spatially aware, low-latency speech separation model specifically designed for children's speech in complex classroom environments, with efficient transfer learning methods.

Findings

Classroom-specific training improves separation quality.

Finetuning with half the data achieves comparable performance.

Diffuse babble noise training enhances robustness.

Abstract

Classroom environments are particularly challenging for children with hearing impairments, where background noise, multiple talkers, and reverberation degrade speech perception. These difficulties are greater for children than adults, yet most deep learning speech separation models for assistive devices are developed using adult voices in simplified, low-reverberation conditions. This overlooks both the higher spectral similarity of children's voices, which weakens separation cues, and the acoustic complexity of real classrooms. We address this gap using MIMO-TasNet, a compact, low-latency, multi-channel architecture suited for real-time deployment in bilateral hearing aids or cochlear implants. We simulated naturalistic classroom scenes with moving child-child and child-adult talker pairs under varying noise and distance conditions. Training strategies tested how well the model adapts to children's speech through spatial cues. Models trained on adult speech, classroom data, and finetuned variants were compared to assess data-efficient adaptation. Results show that adult-trained models perform well in clean scenes, but classroom-specific training greatly improves separation quality. Finetuning with only half the classroom data achieved comparable gains, confirming efficient transfer learning. Training with diffuse babble noise further enhanced robustness, and the model preserved spatial awareness while generalizing to unseen distances. These findings demonstrate that spatially aware architectures combined with targeted adaptation can improve speech accessibility for children in noisy classrooms, supporting future on-device assistive technologies.