This paper introduces a reconfigurable test bed for advanced distributed RF sensing algorithmic prototyping and performance evaluation that demonstrates the feasibility of the QoSMOS project sensing architecture. Both the individual sensors and the controller unit are implemented using the open SDR platform GNU Radio. The sensors use USRP hardware. The current implementation uses the energy detection algorithm in the sensors and the hard 1-out-of-M combination in the controller unit to generate the final decision on the presence of an incumbent signal. All communication between the controller unit and the sensing elements is done over standard IP protocol for ease of use. The test signals imitating the RF scene that the sensors would experience in a real world deployment are created using an Agilent's state-of-the-art radio environment emulator. The significant gains achieved with this distributed sensing test bed are aligned with the theoretical results found in the literature.

The handling of overlapping speech in the context of speaker diarisation attracted in recent years the interest of the scientific community, since speaker overlap was identified as one of the factors degrading the performance of conventional diarisation systems. In this study, the authors are discussing the possibility of using long-term prosodic features for the detection of overlapping speech, which is subsequently employed in speaker diarisation to improve the baseline system. The most relevant subset from the set of candidate prosodic features is determined in two steps. First, a ranking according to minimal-redundancy-maximal-relevance criterion is obtained, and then a hill-climbing wrapper strategy is applied for determining the optimal number of prosodic features, which should accompany short-term spectral features for overlap detection. In experiments on the augmented multi-party interaction (AMI) meeting distant-channel data, the authors show that the addition of prosodic features decreased overlap detection error. Detected overlap segments were used in speaker diarisation to recover missed speech by assigning multiple speaker labels and to increase the purity of speaker clusters. Improvements of the baseline diarisation system are reported in both single- and multi-site data conditions. However, the extension of the diarisation system with TDOAs showed its incompatibility with the overlap exclusion technique.

Simultaneous speech poses a challenging problem for conventional speaker diarization systems. In meeting data, a substantial amount of missed speech error is due to speaker overlaps, since usually only one speaker label per segment is assigned. Furthermore, simultaneous speech included in training data can lead to corrupt speaker models and thus worse segmentation performance. In this paper, we propose the use of three spatial cross-correlation-based features together with spectral information for speaker overlap detection on distant microphones. Different microphone-pair data are fused by means of principal component analysis. We have obtained an improvement of the speaker diarization system over the baseline by discarding overlap segments from model training and assigning two speaker labels to them according to likelihoods in Viterbi decoding. In experiments conducted on the AMI Meeting corpus, we achieve a relative DER reduction of 11.2% and 17.0% for single- and multi-site data, respectively. The improvement of clustering with techniques such as beamforming and TDOA-feature stream also leads to a higher effectiveness of the overlap labeling algorithm. Preliminary experiments with NIST RT data show DER improvement on the RT'09 meeting recordings as well.

Overlapping speech is responsible for a certain amount of errors produced by tandard speaker diarization systems in meeting environment. We are investigating a set of prosody-based long-term features as a potential complement to our overlap detection\nsystem relying on short-term spectral parameters. The most relevant features are selected in a two-step process. They\nare firstly evaluated and sorted according to mRMR criterion and then the optimal number is determined by iterative wrapper\napproach. We show that the addition of prosodic features decreased overlap detection error. Detected overlap segments are used in speaker diarization to recover missed speech by assigning multiple speaker labels and to increase the purity of speaker clusters.

A substantial portion of errors of the conventional speaker\ndiarization systems on meeting data can be accounted to overlapped\nspeech. This paper proposes the use of several spatial\nfeatures to improve speech overlap detection on distant channel\nmicrophones. These spatial features are integrated into a\nspectral-based system by using principal component analysis\nand neural networks. Different overlap detection hypotheses\nare used to improve diarization performance with both overlap\nexclusion and overlap labeling. In experiments conducted\non AMI Meeting Corpus we demonstrate a relative DER improvement\nof 11.6% and 14.6% for single- and multi-site data,\nrespectively.