2 citations · 8 across the 9 of their papers we have counts for
6 papers · 1 filter
Short-time deep-learning based source separation for speech enhancement in reverberant environments with beamforming
Alejandro Díaz, Diego Pincheira, Rodrigo Mahu +1
The source separation-based speech enhancement problem with multiple beamforming in reverberant indoor environments is addressed in this paper. We propose that more generic solutio…
Non causal deep learning based dereverberation
Jorge Wuth, Richard M. Stern, Nestor Becerra Yoma
In this paper we demonstrate the effectiveness of non-causal context for mitigating the effects of reverberation in deep-learning-based automatic speech recognition (ASR) systems.…
On combining features for single-channel robust speech recognition in reverberant environments
José Novoa, Josué Fredes, Jorge Wuth +3
This paper addresses the combination of complementary parallel speech recognition systems to reduce the error rate of speech recognition systems operating in real highly-reverberan…
Weighted delay-and-sum beamforming guided by visual tracking for human-robot interaction
José Novoa, Rodrigo Mahu, Alejandro Díaz +3
This paper describes the integration of weighted delay-and-sum beamforming with speech source localization using image processing and robot head visual servoing for source tracking…
An improved DNN-based spectral feature mapping that removes noise and reverberation for robust automatic speech recognition
Juan Pablo Escudero, José Novoa, Rodrigo Mahu +4
Reverberation and additive noise have detrimental effects on the performance of automatic speech recognition systems. In this paper we explore the ability of a DNN-based spectral f…
Exploring the robustness of features and enhancement on speech recognition systems in highly-reverberant real environments
José Novoa, Juan Pablo Escudero, Jorge Wuth +4
This paper evaluates the robustness of a DNN-HMM-based speech recognition system in highly-reverberant real environments using the HRRE database. The performance of locally-normali…