## Contributions
### Evaluation  
Since the first edition, IberSPEECH has included the _Albayzin_ Evaluation Challenges in different speech technologies such as Spoken Term Detection, Language Identification, or Speech Synthesis. This special issue includes the paper from [PERSON] et al. [14], describing the IberSpeech-RTVE Challenge, organized by Radio Television Espanola, RTVE, the Spanish Public Broadcast Corporation and Vivolab, the speech research group at the University of Zaragoza. This Challenge consists of a set of technological evaluations in the areas of speech recognition and speaker diarization, including multimodal (audio and video) diarization.  
First of all, the paper describes the RTVE 2018 database, a freely-available audiovisual database released for this evaluation by RTVE and the RTVE Chair at the University of Zaragoza. We believe that it will contribute significatively to the progress of Speech Technologies in Spanish, including the dialectal varieties spoken in Latin America. It contains more than 500 h of audio amb subtitles of broadcast data, spanning a broad range of genres. For this challenge, the database is a significant part of the database includes human-revised transcriptions and speaker labels.  
The speech-to-text challenge has been evaluated using 39 h of audio from eight different TV shows, covering a range variety of acoustic conditions and speaker styles. For training, two scenarios were defined. In the close-set scenario, the systems could use approximately 70 h of transcribed speech and 460 h of untranscribed audio that includes subtitles. In the open-set scenario, participants were free to include any data to train their systems. Seven teams participated in the evaluation. The systems trained in the open-set scenario achieved in most of the systems significantly better results. The best Word Error Rate (WER) for open-set condition was 3% smaller than in close-set condition (16.45% vs. 19.57%). It was achieved by hybrid DNN-HMM systems.  
For the speaker diarization challenge, the test data consisted of 22 h from different shows. For the close-set scenario, the same training data described above could be used. It includes 16 h of diarization annotations tracking the identity of the active speaker. The training speech database was extended with more than 100 h from other broadcast media. As in the other challenges, in the open-set scenario, any external data can be used to train the system. In this challenge, the results are similar for both open-set and close-set scenarios. The diarization error of the best system was 11.4% in the open-condition. In this scenario, the best system was much better than the other five participants. The analysis of the results show the huge impact on this metric of having a good estimation of the number of speakers present in the data.  
The multimodal diarization challenge provided not only the speech, but also the video track along with enrollment audio, video, and pictures of the speakers to track. The training data included 2 h of annotated video, where the face of the speaker was identified, and 14 h with the identification of the active speaker. For the test, the system was provided 10 photos and 20 s of video of 39 characters. The smallest multimodal diaritzation error, defined as the mean between face and speaker diarization error, is approximately 23%. Without using the video information, the diarization error is 6% worse (29%). As in speech recognition, there are notable differences between shows, as some shows include frequent speaker overlap and exterior shots.