A Hierarchical End-of-Turn Model with Primary Speaker Segmentation for Real-Time Conversational AI

We present a real-time front-end for voice-based conversational AI to enable natural turn-taking in two-speaker scenarios by combining primary speaker segmentation with hierarchical End-of-Turn (EOT) detection. To operate robustly in multi-speaker environments, the system continuously identifies and tracks the primary user, ensuring that downstream EOT decisions are not confounded by background conversations. The tracked activity segments are fed to a hierarchical, causal EOT model that predicts the immediate conversational state by independently analyzing per-speaker speech features from both the primary speaker and the bot. Simultaneously, the model anticipates near-future states ($t{+}10/20/30$\,ms) through probabilistic predictions that are aware of the conversation partner's speech. Task-specific knowledge distillation compresses wav2vec~2.0 representations (768\,D) into a compact MFCC-based student (32\,D) for efficient deployment. The system achieves 82\% multi-class frame-level F1 and 70.6\% F1 on Backchannel detection, with 69.3\% F1 on a binary Final vs.\ Others task. On an end-to-end turn-detection benchmark, our model reaches 87.7\% recall vs.\ 58.9\% for Smart Turn~v3 while keeping a median detection latency of 36\,ms versus 800--1300\,ms. Despite using only 1.14\,M parameters, the proposed model matches or exceeds transformer-based baselines while substantially reducing latency and memory footprint, making it suitable for edge deployment.

FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for Acoustic Echo Cancellation

Despite the potential of diffusion models in speech enhancement, their deployment in Acoustic Echo Cancellation (AEC) has been restricted. In this paper, we propose DI-AEC, pioneering a diffusion-based stochastic regeneration approach dedicated to AEC. Further, we propose FADI-AEC, fast score-based diffusion AEC framework to save computational demands, making it favorable for edge devices. It stands out by running the score model once per frame, achieving a significant surge in processing efficiency. Apart from that, we introduce a novel noise generation technique where far-end signals are utilized, incorporating both far-end and near-end signals to refine the score model's accuracy. We test our proposed method on the ICASSP2023 Microsoft deep echo cancellation challenge evaluation dataset, where our method outperforms some of the end-to-end methods and other diffusion based echo cancellation methods.