# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 1 Introduction  
The Albayzin evaluations are a series of technological benchmarks and challenges open to the scientific community within different fields of the broad area of speech technologies. Organized every two years since 2006 and supported by the Spanish Thematic Network on Speech Technologies (Red Tematica en Tecnologias del Habla (RTTH), ([[http://www.rtth.es](http://www.rtth.es)]([http://www.rtth.es](http://www.rtth.es)) (accessed on 24 July 2023) ), starting from 2018 the evaluations have focused on the broadcast media area. Thanks to Radio Television Espanola, RTVE ([[http://www.rtve.es](http://www.rtve.es)]([http://www.rtve.es](http://www.rtve.es)) (accessed on 24 July 2023)), the Spanish Public Broadcast Corporation, and the RTVE Chair at the University of Zaragoza (Catedra RTVE de la Universidad de Zaragoza: ([[http://catedrartve.unizar.es](http://catedrartve.unizar.es)]([http://catedrartve.unizar.es](http://catedrartve.unizar.es))(accessed on 24 July 2023)), new and more challenging datasets are being released to support the assessment of speech technologies in broadcast media tasks. Speech technologies have been introduced in this area due to their high potential to automate processes as subtitling and caption generation and alignment, or automatic metadata generation for audiovisual content. This requires continuous efforts to evaluate the performance of these technologies and to support new developments. In the last years, deep learning approaches have completely changed the landscape with a great boost in performance in image recognition, natural language processing and speech recognition applications. This has allowed the introduction of speech technologies in the work pipeline of broadcast archives and documentation services. Different evaluation campaigns for broadcast speech have been carried out from the first one proposed in 1996 [1], most of them using English as the target language [2; 3]. In the past few years, speech evaluations organized by the National Institute of Standards and Technology (NIST) have expanded the range of languages of interest (sometimes including low-resourced languages such as Cantonese, Pashto, Tagalog, Swahili, Tamil, to name a few) and speech domains, such as telephone and public safety communication. These evaluations included automatic speech recognition (ASR), spoken term detection and keyword spotting tasks [4; 5; 6; 7; 8; 9; 10]. The Albayzin evaluations have always focused on Iberian languages, mainly Spanish and to a lesser extent Catalan and Basque. Spanish is the second language with the highest number of native speakers and the fourth most spoken language in the world ([[https://www.ethnologue.com/insights/ethnologue200/](https://www.ethnologue.com/insights/ethnologue200/)]([https://www.ethnologue.com/insights/ethnologue200/](https://www.ethnologue.com/insights/ethnologue200/)) (accessed on 24 July 2023)). The Albayzin evaluations have the purpose of tracking the evolution of speech technologies for Iberian Languages with new and increasingly challenging datasets in each evaluation campaign. In the last editions, the evaluations have focused on Spanish language in the broadcast media sector. In the 2022 edition, four challenges have been proposed:  
* Speech to Text Challenge (S2 TC), organized by RTVE and Universidad de Zaragoza, which consists of automatically transcribing different types of TV shows. * Speaker Diarization (SDC) and Identity Assignment Challenge (SDIAC), organized by RTVE and Universidad de Zaragoza, which consists of segmenting broadcast audio documents according to different speakers, linking those segments which originate from the same speaker and identifying a closed set of speakers. * Text and Speech Alignment Challenge (TaSAC), which consists of two sub-challenges:
* (Task 1) TaSAC-ST: Alignment of re-speaking generated subtitles, organized by RTVE and Universidad de Zaragoza, which consists of synchronizing the broadcast subtitles created by re-speaking of different TV shows.