# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 4 IberSpeech-RTVE 2022 Submitted Systems  
### Speech to Text Challenge  
A total of 13 different systems from four participating teams were submitted. The most relevant characteristics of each system are presented in terms of the recognition engine, and audio and text data used for training acoustic and language models. * **BCN2 BRNO**[21]. BUT Speech@FIT research group (Brno University of Technology, Czech Republic) and Telefonica Research (Spain) BCN2 BRNO submitted a primary system based on a word-level ROVER fusion of five individual models. Three models used an Encoder-Decoder Transformer architecture (XLS-R Conformer, XLSR-53-CTC and Whisper large model), the fourth was an RNN Transducer architecture and the fifth was a hybrid DNNN-HMM model. The ASR pipeline consisted of 4 conceptual blocks: (a) voice activity detection to split audio recordings into smaller segments, (b) ASR models to produce N-best lists of hypotheses and scores, (c) RNN-LM rescoring to produce 1-best hypothesis for each ASR system, and (d) shallow fusion to give a single output. The system achieved 13.7% WER on the official evaluation dataset in the previous Albayzin 2020 Speech to Text Transcription challenge where the best result was 16.04% WER. The data used for training the ASR models contained the RTVE2018, 2020 and 2022 databases and the Spanish Common Voice dataset (400 h of read speech). Noise data augmentation was used with the Spanish Common Voice dataset to produce an augmented data partition with an SNR range from 6 dB to 20 dB and different noises (restaurant, street, home, workshop, fan, and non-speech segments longer than 2 s from the RTVE dataset). The language model used for rescoring was trained on a collection of Spanish newspapers' texts and fine-tuned to the transcripts of the training data. Three contrastive systems were submitted: (c1) the fusion of an XLSR-128-Conformer with data augmentation and a CNN-TDNN-HMM with x-vectors; (c2) a single system based on XLSR-128-Conformer, and (c3) the Whisper large model. * **UZ**[22]. ViVoLab research group (University of Zaragoza, Spain)The ViVoLab system was a combination of several subsystems designed to perform a full subtitle edition process from the raw audio to the creation of aligned subtitle transcribed partitions. The subsystems included a phonetic recognizer, a phonetic subword recognizer, a speaker-aware subtitle partitioner, a sequence-to-sequence translation model working with orthographic tokens to produce the desired transcription, and an optional diarization step with the previously estimated segments. Additionally, the system used recurrent network-based language models to improve results for steps that involve search algorithms like the subword decoder and the sequence-to-sequence model. The technologies involved include unsupervised models like Wavlm to deal with the raw waveform, convolutional, recurrent, and transformer layers. The acoustic models were trained using the following datasets: (a) Albayzin, a phonetically balanced corpus in Spanish with 12 h, (b) Speech-Lat-Car, a corpus recorded in a car in different driving conditions with 18 h, (c) Domolab, a corpus recorded in a domotic environment with 9 h, (d) TCSTAR transcriptions of Spanish parliament sessions with 111 h, (e) Common Voice in Spanish with 400 h and (f) RTVE 2018 train, dev and RTVE 2022 train with more than 800 h. In addition, more training material was added from a diverse scarp of online videos and social networks with a total of 10,000 h. These data were filtered after performing forced alignment and selecting transcribed segments with sufficient quality. Data augmentation was used including noises, music and artificially generated impulse responses to simulate different acoustic environments. The language model was trained using Spanish Wikipedia, RTVE challenge subtitles, and text obtained from different Spanish newspapers.