## 4 Submitted Systems
### Speaker Diarization
#### 4.2.1 Open-Set Condition Systems  
* G1-GTM-UVIGO. Multimedia Technologies Group, Universidad de Vigo, Spain. A pre-trained deep neural network [[http://kaldi-asr.org/models.html](http://kaldi-asr.org/models.html)]([http://kaldi-asr.org/models.html](http://kaldi-asr.org/models.html)) was used with Kaldi and data from VoxCeleb1 and VoxCeleb2 databases [[http://www.robots.ox.ac.uk/~vgg/data/](http://www.robots.ox.ac.uk/~vgg/data/)]([http://www.robots.ox.ac.uk/~vgg/data/](http://www.robots.ox.ac.uk/~vgg/data/))voxceleb/. This DNN mapped variable length speech segments into fixed dimension vectors called x-vectors. The strategy followed for diarization consisted of three main stages. The first stage was the extraction of the x-vector and grouping using the clustering algorithm \"Chinese Whispers\". In the second stage, each of the audio segments was processed to extract one or more x-vectors using a sliding window of 10 s with half a second of displacement between successive windows. These vectors were grouped using the clustering algorithm \"Chinese Whispers\" obtaining the clusters that defined the result of the diarization. Finally, a music/non-music discriminator based on i-vectors and a logistic regression model were applied to eliminate those audio segments that were highly likely to correspond to music. This discriminator was also trained with external data. * G11-ODESSA [25]. EURECOM, LIMSI, CNRS, France. A primary system resulting from the combination at a similarity matrix level of three systems, one trained according to the closed-set condition and two trained with two external databases (NIST SRE (Speaker Recognition Evaluation) and Voxceleb), was submitted. The first contrastive system used one second uniform segmentation, x-vector representation trained on NIST SRE data, and agglomerative hierarchical clustering (AHC). The second contrastive system was the same as the one for the closed-set, but where the training data were replaced with the Voxceleb data. * G20-STAR-LAB [26]. STAR Lab, SRI International, USA. The training signals were extracted from the databases NIST SRE [[https://www.nist.gov/itl/iad/mig/speaker-recognition](https://www.nist.gov/itl/iad/mig/speaker-recognition)]([https://www.nist.gov/itl/iad/mig/speaker-recognition](https://www.nist.gov/itl/iad/mig/speaker-recognition)) 2004-2008, NIST SRE 2012, Mixer6 [[https://catalog.ldc.upenn.edu/LDC2013S03](https://catalog.ldc.upenn.edu/LDC2013S03)]([https://catalog.ldc.upenn.edu/LDC2013S03](https://catalog.ldc.upenn.edu/LDC2013S03)), Voxceleb1, and Voxceleb2. Augmentation of data was applied using four categories of degradations including music, noise at a 5 dB signal-to-noise ratio, compression, and low levels of reverb. STAR-Lab used the embeddings and diarization system developed for the speaker recognition competition NIST 2018 [27]. It incorporated modifications in the detection of voice activity and in the calculation of embeddings for speaker recognition. The differences between systems, primary and contrast, were found in the different parameters used in the voice activity detection system. * **G21-EMPHATIC**[28]. SPIN-Speech Interactive Research Group, Universidad del Pais Vasco, Spain, Intelligent Voice, U.K. The Switchboard corpora databases (LDC2001S13, LDC2002S06, LDC2004S07, LDC98S75, LDC99S79) and NIST SRE 2004-2010 were used for training. Data augmentation was used to provide a greater diversity of acoustic environments. The representation of speaker embeddings was obtained through an end-to-end model using convolutional (CNN) and recurrent (LSTM) networks. * G22-JHU [29]. Center for Language and Speech Processing, Johns Hopkins University, USA. Results were presented with different databases for training. Voxceleb1 and Voxceleb2 were used with and without data augmentation, SRE12-micphn, MX6-micph, and SITW-dev-core, Fisher database, Albayzin2016, and RTVEDB2018.