Search Results for author:
Found 13 papers, 10 papers with code
DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech Recognition
By leveraging diarization outputs as conditioning signals, DiCoW simplifies the workflow for multi-speaker ASR, improves generalization to unseen speakers and enables more reliable transcription in real-world multi-speaker recordings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Joint Training of Speaker Embedding Extractor, Speech and Overlap Detection for Diarization
In spite of the popularity of end-to-end diarization systems nowadays, modular systems comprised of voice activity detection (VAD), speaker embedding extraction plus clustering, and overlapped speech detection (OSD) plus handling still attain competitive performance in many conditions.
Leveraging Self-Supervised Learning for Speaker Diarization
In this work, we explore using WavLM to alleviate the problem of data scarcity for neural diarization training.
From Modular to End-to-End Speaker Diarization
More recently, the advent of end-to-end models, capable of dealing with all aspects of speaker diarization with a single model and better performing regarding overlapped speech, has brought high levels of attention.
Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio
As a pioneering study in spoof diarization, we focus on defining the task, establishing evaluation metrics, and proposing a benchmark model, namely the Countermeasure-Condition Clustering (3C) model.
DiaPer: End-to-End Neural Diarization with Perceiver-Based Attractors
Until recently, the field of speaker diarization was dominated by cascaded systems.
Discriminative Training of VBx Diarization
Bayesian HMM clustering of x-vector sequences (VBx) has become a widely adopted diarization baseline model in publications and challenges.
DiaCorrect: Error Correction Back-end For Speaker Diarization
In this work, we propose an error correction framework, named DiaCorrect, to refine the output of a diarization system in a simple yet effective way.
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods.
Multi-Speaker and Wide-Band Simulated Conversations as Training Data for End-to-End Neural Diarization
End-to-end diarization presents an attractive alternative to standard cascaded diarization systems because a single system can handle all aspects of the task at once.
From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization
However, simulated mixtures do not resemble real conversations in many aspects.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
BUT System Description for DIHARD Speech Diarization Challenge 2019
This paper describes the systems developed by the BUT team for the four tracks of the second DIHARD speech diarization challenge.
