7 accepted papers
Spoken dialogues with and between voice agents are becoming increasingly common, yet assessing them for their socially harmful content such as violence, harassment, and hate remains text-centric and fails to account for audio-specific cues and transcription errors. We present LALM-as-a-Judge, the fi…
Objective assessment of audio source‑separation systems still mismatches subjective human perception, especially when interference from competing talkers and distortion of the target signal interact. We introduce Perceptual Separation (PS) and Perceptual Match (PM), a complementary pair of measures…
We recently introduced the Efficient User-centric Residual-Echo Suppression (E-URES) framework, which significantly reduces the floating-point operations per second (FLOPS) required during inference by 90% compared to the URES framework. The E-URES operates based on a user-operating point (UOP) defi…
We propose a general framework for adaptation control using deep neural networks (NNs) and apply it to acoustic echo cancellation (AEC). First, the optimal step-size that controls the adaptation is derived offline by solving a constrained nonlinear optimization problem that minimizes the adaptive fi…
Residual-echo suppression (RES) systems suppress the echo and preserve the speech from a mixture of the two. In hands-free speech communication, RES may also be addressed as a source separation (SS) or speech enhancement (SE) problem, where the echo can be manipulated as an interfering speech signal…
In this paper, we propose a residual echo suppression method using a UNet neural network that directly maps the outputs of a linear acoustic echo canceler to the desired signal in the spectral domain. This system embeds a design parameter that allows a tunable tradeoff between the desired-signal dis…
State-of-the-art deep-learning-based voice activity detectors (VADs) are often trained with anechoic data. However, real acoustic environments are generally reverberant, which causes the performance to significantly deteriorate. To mitigate this mismatch between training data and real data, we simul…