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Found 9 papers, 1 papers with code
The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System
We present the NVIDIA NeMo team's multi-channel speech recognition system for the 7th CHiME Challenge Distant Automatic Speech Recognition (DASR) Task, focusing on the development of a multi-channel, multi-speaker speech recognition system tailored to transcribe speech from distributed microphones and microphone arrays.
Property-Aware Multi-Speaker Data Simulation: A Probabilistic Modelling Technique for Synthetic Data Generation
This capability offers a tailored training environment for developing neural models suited for speaker diarization and voice activity detection.
Enhancing Speaker Diarization with Large Language Models: A Contextual Beam Search Approach
In addition, these findings point to the potential of using LLMs to improve speaker diarization and other speech processing tasks by capturing semantic and contextual cues.
Multi-scale Speaker Diarization with Dynamic Scale Weighting
First, we use multi-scale clustering as an initialization to estimate the number of speakers and obtain the average speaker representation vector for each speaker and each scale.
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Federated Learning is a fast growing area of ML where the training datasets are extremely distributed, all while dynamically changing over time.
A Review of Speaker Diarization: Recent Advances with Deep Learning
Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify "who spoke when".
Speaker Diarization with Lexical Information
This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap
In this study, we propose a new spectral clustering framework that can auto-tune the parameters of the clustering algorithm in the context of speaker diarization.
Ranked #1 on
Speaker Diarization
on CALLHOME
(DER(ig olp) metric)
Speaker Diarization With Lexical Information
This work presents a novel approach to leverage lexical information for speaker diarization.
