# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 1 Introduction  
Originally, Automatic Speech Recognition (ASR) was focused solely on acoustic cues [1; 2]. Although today these auditory-based ASR systems are capable of understanding spoken language with outstanding quality [3; 4], their performance deteriorates in adverse scenarios such as noisy environments [5; 6; 7]. Hence, influenced by different studies that have shown the relevance of visual cues during speech perception [8; 9], the robustness of ASR systems has been enhanced by the design of audiovisual approaches [6; 7; 10; 11]. In addition, these studies have encouraged the development of systems capable of interpreting speech by reading only the lips of the speaker. This challenging task, known as Visual Speech Recognition (VSR), has been a focus of interest during the last few decades [12]. Moreover, recognising speech without the need for acoustic stream data offers a wide range of applications, such as silent speech passwords [13], visual keyword spotting [14], or the development of silent speech interfaces that would be able to improve the lives of people who experience difficulties in producing speech [15; 16; 17]. Unprecedented advances have recently been achieved in the field of VSR thanks to the availability of large-scale audiovisual databases [18; 19; 20] and the design of end-to-end architectures [6; 7; 11; 21; 22]. Specifically, by combining the Connectionist Temporal Classification (CTC) [23] with Attention [24] paradigms, the so-called hybrid CTC/Attention architecture [25] stands as the current state-of-the-art in the field, reaching performanceof around 25-30% Word Error Rate (WER) [22]. However, VSR remains an open research problem; by dispensing with the auditory sense, different challenges must be considered, e.g., visual ambiguities [26; 27], the complex modeling of silence [28], the intra-personal variability among speakers [29], and different lighting conditions, as well as more technical aspects such as frame rate and image resolution [30; 31; 32]. [PERSON] et al. [33] argued that only 30% of speech information is visible, which highlights the relevance of modeling contextual relationships when addressing VSR [12]. These challenges can be alleviated when the VSR task is approached from a speaker-dependent perspective [34; 35]. In fact, it has been proven that each person produces speech in a unique way [36], a finding that supports the idea that visual speech features are highly sensitive to the identity of the speaker [29]. However, although a wide range of works have studied the speaker adaptation of end-to-end systems in the field of ASR [37; 38; 39; 40], only a few works in this regard have addressed VSR [41; 42]. Although this speaker-dependent approach makes for a less demanding task, it should not be forgotten that speaker-adapted VSR systems could be helpful in a non-invasive and inconspicuous way for people who suffer from communication difficulties [15; 16; 17]. Another important aspect to highlight is that the most predominant approach when adapting a model to new languages or domains is to re-estimate all the parameters that compose it [22; 43]. However, this technique, known as fine-tuning, requires estimating and maintaining a separate model for each task for which it is to be adapted, incurring additional time and storage costs. For this reason, [PERSON] and [PERSON] [44] proposed the so-called Adapters as a scalable and parameter-efficient adaptation method. Although different works in ASR have been based on this method to address language- or domain-adaptation tasks [45; 46], to the best of our knowledge it has not yet been explored for the field of VSR. Details on these adaptation methods can be found in Section 5. In addition, languages other than English have recently received increasing interest in the field of VSR [20; 22; 47; 48]. In these cases, the lack of available audiovisual resources [12] can be considered an additional challenge. **Contributions:** Our research focuses on the adaptation of end-to-end VSR systems to a specific speaker.