# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 4 IberSpeech-RTVE 2022 Submitted Systems
### Speech to Text Challenge
The system achieves 21.86% WER on the official evaluation dataset in the previous Albayzin 2020 Speech to Text Transcription challenge, where the best result was 16.04% WER. * **TID**[23]. Telefonica I + D (Spain) The TID system consisted of an acoustic end-to-end ASR based on the XLSR-53 model pre-trained with 56k hours of audio in 53 languages and fine-tuned in the Spanish Common Voice dataset. A voice activity detection (VAD) model was used to filter non-speech segments. The transcription was obtained using simple greedy decoding from the frame-wise character posteriors given by the model. A self-supervised method was used to adapt the ASR to the TV broadcast domain by retrieving data from the RTVE2018 and 2022 datasets. A neural machine translation model was used as an external language model to correct the ASR hypothesis. The primary system used VAD segments and corrected the output with the language model. Three additional contrastive systems were submitted: (c1) just used the VAD segments, (c2) used 10-second-length window segmentation and corrected the output with the language model, and (c3) generated the output using a 10-s-length segmentation window size. * **VICOM-UPM**[24]. Fundacion Vicomtech (Basque Research and Technology Alliance) and Polytechnic University of Madrid (Spain) VICOM-UPM submission consisted of a total of 4 different systems which allowed testing state-of-the-art modeling approaches focused on different learning techniques and typologies of neural networks. As primary systems, VICOM-UPM presented the pre-trained Wav2 Vec2-XLS-R-1B model adapted with 300 h of in-domain data from the RTVE2018 and RTVE2020 datasets. The first contrastive system corresponded to a pruned RNN-Transducer model, composed of a Conformer encoder and a stateless prediction network using BPE word-pieces as output symbols. The second contrastive system was a Multistream-CNN acoustic model-based system with a non-pruned 3-gram model for decoding and an RNN-based language model for rescoring the initial lattices. Finally, the third contrastive system was the publicly available Large model of the Whisper engine. The acoustic corpus was composed of annotated audio contents from 9 different datasets, summing up a total of 1927 h. The datasets were: (a) RTVE2018, (b) SAVAS corpus (broadcast news contents in Spanish of the Basque Country's public broadcast corporation), (c) IDAZLE corpus (TV shows from the Basque Country's public broadcast corporation), (d) RTVE Play 2020 and 2022 including programs broadcast between 2018 and 2022 by RTVE, (e) RTVE youtube including contents of RTVE from the YouTube platform, (f) Spanish Common Voice, (g) Albayzin, and (h) Multext (multilingual prosodic database). The text corpus consisted of transcriptions of the training datasets, RTVE2018 subtitles, RTVE play and news subtitles and generic news gathered from digital newspapers on the Internet. With regard to the RTVE 2020 test dataset, the best results were obtained by the Whisper model (12.15% WER) followed by the primary system (13.77%).