Zero-shot test time adaptation via knowledge distillation for personalized speech denoising and dereverberation.

A personalization framework to adapt compact models to test time environments and improve their speech enhancement (SE) performance in noisy and reverberant conditions is proposed. The use-cases are when the end-user device encounters only one or a few speakers and noise types that tend to reoccur in the specific acoustic environment. Hence, a small personalized model that is sufficient to handle this focused subset of the original universal SE problem is postulated. The study addresses a major data shortage issue: although the goal is to learn from a specific user's speech signals and the test time environment, the target clean speech is unavailable for model training due to privacy-related concerns and technical difficulty of recording noise and reverberation-free voice signals. The proposed zero-shot personalization method uses no clean speech target. Instead, it employs the knowledge distillation framework, where the more advanced denoising results from an overly large teacher work as pseudo targets to train a small student model. Evaluation on various test time conditions suggests that the proposed personalization approach can significantly enhance the compact student model's test time performance. Personalized models outperform larger non-personalized baseline models, demonstrating that personalization achieves model compression with no loss in dereverberation and denoising performance.

Phase-based dual-microphone robust speech enhancement.

A dual-microphone speech-signal enhancement algorithm, utilizing phase-error based filters that depend only on the phase of the signals, is proposed. This algorithm involves obtaining time-varying, or alternatively, time-frequency (TF), phase-error filters based on prior knowledge regarding the time difference of arrival (TDOA) of the speech source of interest and the phases of the signals recorded by the microphones. It is shown that by masking the TF representation of the speech signals, the noise components are distorted beyond recognition while the speech source of interest maintains its perceptual quality. This is supported by digit recognition experiments which show a substantial recognition accuracy rate improvement over prior multimicrophone speech enhancement algorithms. For example, for a case with two speakers with a 0.1 s reverberation time, the phase-error based technique results in a 28.9% recognition rate gain over the single channel noisy signal, a gain of 22.0% over superdirective beamforming, and a gain of 8.5% over postfiltering.