# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 8 Conclusions and Future Work  
In this work, we have addressed the adaptation of end-to-end VSR systems to specific speakers. We propose two adaptation methods, one based on the use of the so-called Adapters approach and the other based on the conventional fine-tuning technique, with three different strategies defined in total. Then, we conducted a thorough comparative study, reaching the conclusion that, while both approaches provide recognition rates comparable to the state of the art, the Adapters-based method presents a more scalable and efficient solution for deployment purposes. Although using this method implies a deterioration in terms of performance, in our case study it significantly reduced the training time and storage cost by up to 80% with respect to the full-model fine-tuning technique.  
For future work, as suggested in [45], we are considering further research regarding the Adapters-based method and ways to improve its performance. For instance, by exploiting the flexibility that this method offers, it might be feasible to study the use of Adapter modules with different inner projection sizes depending on the layer where they were injected. In addition, taking into account the deployment of these VSR systems for real applications, we intend to consider focusing our research on reducing the real-time factor when decoding speech, as well as on exploring streaming speech recognition. The former could be addressed by the design of non-autoregressive architectures [23; 70], while for the latter, a consideration could be the use of Recurrent Neural Network Transducers [71; 72].  
Conceptualization, [PERSON]-G. and C.-D.M.-H.; methodology, [PERSON]-G. and C.-D.M.-H.; software, D.G.-G.; validation, D.G.-G. and C.-D.M.-H.; formal analysis, [PERSON]-G. and C.-D.M.-H.; investigation, [PERSON]-G. and C.-D.M.-H.; resources, C.-D.M.-H.; data curation, [PERSON]-G. and C.-D.M.-H; writing--original draft preparation, [PERSON]-G. and C.-D.M.-H.; writing--review and editing, [PERSON]-G. and C.-D.M.-H.; visualization, [PERSON]-G. and C.-D.M.-H.; supervision, C.-D.M.-H.;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **\% WER** & **Training Time \({}^{\ddagger}\)** & **Storage** \\ \hline
**TS-SAT** & 24.9 \(\pm\) 1.4 & 78.1 & 4.2 \\
**Best Adapter** & 32.9 \(\pm\) 1.3 & 13.6 & 0.611 \\ \hline \hline \end{tabular} \({}^{\dagger}\) It should be noted that both methods are based on the MST model (see Section 5.1), which implies a training process of approximately 2 h.  
\end{table}
Table 3: Overall comparison of the best approach for each proposed adaptation method. Only experiments where the training dataset was used are considered. _Best Adapter_ refers to the approach for each considered speaker where the adapter size with the best recognition rate was used. Training times are reported in minutes (min). Storage costs are reported in Gigabytes (GB). All reported details were computed considering the estimation of twenty speaker-adapted VSR systems.  
project administration, C.-D.M.-H.; funding acquisition, C.-D.M.-H. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Grant CIACIF/2021/295 funded by Generalitat Valenciana and by the Grant PID2021-124719 OB-I00 under the LLEER (PID2021-124719 OB-I00) project funded by MCIN/AEI/10.13039/501100011033/ and by ERDF EU, \"A way of making Europe\".  
Not applicable.  
The authors declare no conflict of interest.