21 citations · 64 across the 43 of their papers we have counts for
5 papers · 1 filter
Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet
Anyu Ying, Natarajan Balaji Shankar, Chyi-Jiunn Lin +7
Despite advancements in ASR, child speech recognition remains challenging due to acoustic variability and limited annotated data. While fine-tuning adult ASR models on child speech…
Practical applicability of deep neural networks for overlapping speaker separation
Pieter Appeltans, Jeroen Zegers, Hugo Van hamme
This paper examines the applicability in realistic scenarios of two deep learning based solutions to the overlapping speaker separation problem. Firstly, we present experiments tha…
CNN-LSTM models for Multi-Speaker Source Separation using Bayesian Hyper Parameter Optimization
Jeroen Zegers, Hugo Van hamme
In recent years there have been many deep learning approaches towards the multi-speaker source separation problem. Most use Long Short-Term Memory - Recurrent Neural Networks (LSTM…
Memory Time Span in LSTMs for Multi-Speaker Source Separation
Jeroen Zegers, Hugo Van hamme
With deep learning approaches becoming state-of-the-art in many speech (as well as non-speech) related machine learning tasks, efforts are being taken to delve into the neural netw…
Multi-scenario deep learning for multi-speaker source separation
Jeroen Zegers, Hugo Van hamme
Research in deep learning for multi-speaker source separation has received a boost in the last years. However, most studies are restricted to mixtures of a specific number of speak…