Rodrigo Mahu

José Novoa, Rodrigo Mahu, Alejandro Díaz et al.

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. We take into consideration the fact that the directivity gain provided by the beamforming depends on the angular distance between its main lobe and the main response axis of the microphone array. A visual servoing scheme is used to reduce the angular distance between the center of the video frame of a robot camera and a target object. Additionally, the beamforming strategy presented combines two information sources: the direction of the target object obtained with image processing and the audio signals provided by a microphone array. These sources of information were integrated by making use of a weighted delay-and-sum beamforming method. Experiments were carried out with a real mobile robotic testbed built with a PR2 robot. Static and dynamic robot head as well as the use of one and two external noise sources were considered. The results presented here show that the appropriate integration of visual source tracking with visual servoing and a beamforming method can lead to a reduction in WER as high as 34% compared to beamforming alone.

Juan Pablo Escudero, José Novoa, Rodrigo Mahu et al.

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 feature mapping to remove the effects of reverberation and additive noise. Experiments with the CHiME-2 database show that this DNN can achieve an average reduction in WER of 4.5%, when compared to the baseline system, at SNRs equal to -6 dB, -3 dB, 0 dB and 3 dB, and just 0.8% at greater SNRs of 6 dB and 9 dB. These results suggest that this DNN is more effective in removing additive noise than reverberation. To improve the DNN performance, we combine it with the weighted prediction error (WPE) method that shows a complementary behavior. While this combination provided a reduction in WER of approximately 11% when compared with the baseline, the observed improvement is not as great as that obtained using WPE alone. However, modifications to the DNN training process were applied and an average reduction in WER equal to 18.3% was achieved when compared with the baseline system. Furthermore, the improved DNN combined with WPE achieves a reduction in WER of 7.9% when compared with WPE alone.

Juan Pablo Escudero, Victor Poblete, José Novoa et al.

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 Highly-Reverberant Real Environment database (HRRE). This database contains 13.4 hours of data recorded in real reverberant environments and consists of 20 different testing conditions which consider a wide range of reverberation times and speaker-to-microphone distances. These evaluation sets were generated by re-recording the clean test set of the Aurora-4 database which corresponds to five loudspeaker-microphone distances in four reverberant conditions.

José Novoa, Juan Pablo Escudero, Josué Fredes et al.

In real human robot interaction (HRI) scenarios, speech recognition represents a major challenge due to robot noise, background noise and time-varying acoustic channel. This document describes the procedure used to obtain the Multichannel Robot Speech Recognition Database (MChRSR). It is composed of 12 hours of multichannel evaluation data recorded in a real mobile HRI scenario. This database was recorded with a PR2 robot performing different translational and azimuthal movements. Accordingly, 16 evaluation sets were obtained re-recording the clean set of the Aurora 4 database in different movement conditions.