## 6 Conclusions  
The IberSpeech-RTVE 2018 challenge was a new edition of the Albayzin evaluation series focused on speech-to-text transcription, speaker diarization, and multimodal diarization of TV broadcast content. A broad international participation was achieved with 18 teams, five of them participating in more than one task. A new dataset named the RTVE2018 database was released containing more than 500 h of TV shows.  
Seven teams participated in the speech-to-text task submitting 18 different systems, 12 for the open-set condition and 6 for the closed-set condition. The evaluation was carried out over eight different shows covering a broad range of acoustic conditions. The performance of the submitted systems presented a high variability across the different shows that were included in the test set, ranging between 7.43% and 34.45% for the best system in the open-set condition. The most accurate system obtained an overall WER of 16.56%. The closed-set condition obtained worse results due to the difficulty of getting correctly transcribed audio out of the training dataset. The best result, 19.57% WER, was obtained training the acoustic model with 350 h of human-transcribed audio. Text normalization removing stop-words and lemmatization gave a small improvement in terms of WER.  
In the speaker diarization task, nine teams submitted 26 different systems, 13 for each condition, closed-set and open-set. The evaluation was carried out over four different shows covering a broad area of acoustic conditions, number of different speakers, and amount of overlapping speech. The best systems in the open-set and closed-set conditions obtained 11.4% and 17.3% of DER, respectively. There was a big gap in terms of DER between the best system and the rest in both conditions. The success was mainly due to the accuracy in the estimation of the number of speakers for each show. The rest of the submitted system made a clear underestimation of the number of speakers.  
The multimodal diarization challenge achieved the participation of four teams. The test set was composed of two different shows, one episode of a live show (La Manana) and two episodes of a talk show about politics and the economy. The task consisted of identifying voices and faces that belonged to persons from the closed-set of 39 different characters. The most accurate system obtained a multimodal DER of 23.4%. In terms of individual modalities, face and speech, the best results were 17.6% of speaker DER and 29.2% of face DER. In the evaluation, always the speech modality obtained lower DER values than the face modality.  
We plan to continue organizing a new Albayzin evaluation edition in the next IberSpeech conference in 2020. An extension of the database with new annotated audiovisual material for the development and evaluation of new audiovisual technologies is under preparation.  
Conceptualization, E.L., A.O., A.M., V.B.-G., C.P., M.G., and A.d.P.; formal analysis, E.L., A.O., and A.M.; resources, V.B.-G., C.P., M.G., and A.d.P.; writing, original draft preparation, E.L., A.O., and A.M.; writing, review and editing, E.L., A.O., A.M., and V.B.-G.  
This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Social Fund through the project TIN2017-85854-C4-1-R, the Government of Aragon (Reference Group T36_17R) and co-financed with Feder 2014-2020 \"Building Europe from Aragon\", Radio Television Espanola through the RTVE Chair at the University of Zaragoza, and Red Tematica en Tecnologias del Habla 2017, (TEC2017-90829-REDT) founded by Ministerio de Ciencia, Innovacion y Universidades.  
We gratefully acknowledge the support of the IberSpeech 2018 organizers.  
The authors declare no conflict of interest.