2 citations · 4 across the 5 of their papers we have counts for
5 papers · 1 filter
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…