# Special Issue on IberSPEECH 2022: Speech and Language Technologies for Iberian Languages
## Contributions
### Automatic Speech Recognition  
Automatic Speech Recognition (ASR) is a technology that transforms human speech into readable text. This field has grown exponentially over the past decade, with ASR systems popping up in popular applications we use every day such Google Meet or Zoom for meeting transcriptions, and many more. Originally, ASR was focused solely on acoustic cues. Visual Speech Recognition (VSR) allows the development of complementary and/or alternative approaches to ASR to transcribe speech without the need for acoustic information, becoming instrumental to address those challenges posed by noisy communication environments as well as to develop silent speech interfaces for people with speech impairments, for instance. However, the interpretation of visual speech presents specific challenges such as visual ambiguities, the sensibility to changes on lighting conditions and the effect of inter- and intra-speaker variability, among others.  
The article coauthored by [PERSON] and [PERSON] (contribution 2) proposes the adaptation of end-to-end VSR systems to a specific speaker by introducing two adaptation techniques based on the so-called _Adapters_[2] to implement the fine-tuning module after speaker identification. The experiments conducted on the Spanish LIP-RTVE database [3] show that, on the one hand, both methods obtain similar recognition rates to those from state-of-the-art alternatives; a result especially relevant when a limited amount of speaker-dependent training data is available. On the other hand, the authors highlight the scalability of their approach with respect to the full-model fine-tuning techniques, reducing the training time and storage costs by up to 80% -thanks to the reduction of the number of learnable parameters-, but at the cost of a slight worsening their word error rate.  
A study on classifying phones (speech sound) using electromyographic (EMG) signals obtained from the Spanish ReSSInt-EMG database is presented by [PERSON] et al. (contribution 3). This database is part of the ReSSInt project [4], which aims to restore speech for laryngectomees using an EMG-based Silent Speech Interface (SSI). Compared to previous studies on this topic, in this paper the authors revised the linear discriminant analysis (LDA) feature reduction procedure, which resulted in changing the number of LDA features from 28 to 21. This features reduction helps to reduce the training time and the complexity of the model, but the accuracy obtained remains similar.  
The new study is conducted including new sessions and extending the experiments with different modalities regarding speaker and session dependency. Instead of a bagging classifier, a neural network is used as a classification method. The results obtained suggest that the development of an EMG-based SSI with sufficient performance for real-world applications requires a large and diverse database.  
In the paper by [PERSON] et al. (contribution 5), a semisupervised speech data extraction method is presented and applied to create a new dataset designed for the development of fully bilingual Automatic Speech Recognition (ASR) systems for Basque and Spanish. The data collection are from plenary sessions of the Basque Parliament and is used to train domain-adapted models. Also, the main features of a fully bilingual ASR system for Basque and Spanish are presented. This system is based on the integration of acoustic, lexical and language models.  
The presented ASR system is able to deal with code switching [5] events (in bilingual communities, speakers sometimes mix languages and jump spontaneously from one language to another) in a natural and computationally efficient fashion. Performance results, Word Error Rates (WER), show a considerable reduction compared with the used baseline system.