8 citations · 14 across the 9 of their papers we have counts for
9 papers
Learnable Front Ends Based on Temporal Modulation for Music Tagging
Yinghao Ma, Richard M. Stern
While end-to-end systems are becoming popular in auditory signal processing including automatic music tagging, models using raw audio as input needs a large amount of data and comp…
A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement
Tyler Vuong, Yangyang Xia, Richard M. Stern
We describe a modulation-domain loss function for deep-learning-based speech enhancement systems. Learnable spectro-temporal receptive fields (STRFs) were adapted to optimize for a…
Learnable Spectro-temporal Receptive Fields for Robust Voice Type Discrimination
Tyler Vuong, Yangyang Xia, Richard Stern
Voice Type Discrimination (VTD) refers to discrimination between regions in a recording where speech was produced by speakers that are physically within proximity of the recording…
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…
Highly-Reverberant Real Environment database: HRRE
Juan Pablo Escudero, Victor Poblete, José Novoa +5
Speech recognition in highly-reverberant real environments remains a major challenge. An evaluation dataset for this task is needed. This report describes the generation of the Hig…