arXiv:1707. 00835v1 Announce Type: cross Abstract: This contribution gives an overview of face recogni-tion algorithms, their implementation and practical uses.
arXiv:2610.33127v1 Announce Type: cross Abstract: Active speaker detection (ASD) relies on audiovisual correspondence between speech and visible faces, yet this correspondence becomes unreliable in c...
arXiv:2609.26648v1 Announce Type: cross Abstract: Active speaker detection (ASD) requires reliable association between visible faces and acoustic speech, yet existing systems often degrade under chal...
Speaker recognition has advanced rapidly with large-scale training datasets, yet Vietnamese remains under-resourced, with existing corpora limited in scale and acoustic diversity. Most large-scale datasets rely on facial cues to link speech with speaker identities, restricting data collection to recordings where speakers appear on camera.
arXiv:2606. 29335v1 Announce Type: cross Abstract: Multimodal speaker identification systems face two key challenges in real-world deployment: missing modalities and language mismatch between training and testing conditions.
arXiv:2505. 21954v2 Announce Type: replace-cross Abstract: We present UniTalk, a novel dataset emphasizing challenging scenarios to enhance model generalization for the task of active speaker detection (ASD).
The article explains how to develop a speaker‑recognition application using Claude code or Codex, focusing on practical coding techniques for building an internal tool. It provides step‑by‑step guidance on implementing the necessary components and integrating them into a functional app. The post serves as a tutorial for developers looking to create voice‑based authentication or identification systems.
The paper addresses challenges in extracting target and multiple speakers from real conversational speech, noting that real conversations contain more silence and enrolment samples that differ from the target speech. It introduces a new loss function that reduces the impact of excess silence during training, yielding improvements in STOI (from 0.55 to 0.60) and frequency‑weighted segmental SNR (from 4.35 to 5.12). The study also investigates how mismatches between enrolment and target speech affect performance.
FASTDIAR is a frame‑level speaker encoder designed for real‑time streaming diarization on a single CPU thread. It converts a state‑of‑the‑art speaker recognition architecture into a causal model that emits an embedding every 80 ms from a two‑second window, and pairs it with online clustering that updates only when the stream’s self‑similarity changes. Trained via distillation from an utterance‑level teacher, it achieves the highest accuracy on low‑overlap benchmarks, degrades less with more speakers, and runs five times faster than real time.
arXiv:2606. 05931v1 Announce Type: cross Abstract: When retrieving a person from a video archive by voice and face, should the system be multimodal or not?
arXiv:2609.30631v1 Announce Type: cross Abstract: Online audio-visual target-speaker extraction aims to remove competing voices while preserving speech quality and bounding lookahead. Existing extrac...
The paper introduces CF-AVSS, a two‑stage audio‑visual speech separation framework that first generates coarse estimates using a synchronized visual‑stream SPAV‑TFGridNet, then refines these estimates with a TFGridNet‑based target‑speaker refinement network. The final output is a fusion of coarse and refined estimates calibrated on a development set. In the ISCSLP 2026 Real‑World AVSE Challenge, the system tied for first place on Track 1 and ranked second on Track 2, achieving the lowest character error rates of 12.31 % and 14.79 % respectively.
arXiv:2606. 08843v1 Announce Type: cross Abstract: We present a voice conversion (VC) framework that utilizes K-Nearest Neighbors (KNN) retrieval over WavLM representations to align non-parallel source and target speech, constructing synthetic training pairs for supervised learning.