# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 7 Results & Discussion
### Fine-Tuning  
As described in Section 5.1, three different fine-tuning strategies are proposed in our work. Table 2 reflects a comparison between these strategies in general terms. In addition, as described in Section 6.2, we explored the use of different datasets when estimating these fine-tuning strategies. Hence, we studied how robust each proposed strategy is against data scarcity scenarios. Taking into account all these aspects and the results reported in Table 2, we following conclusions can be inferred:  
* Irrespective of the dataset used for estimation, it can be observe that the MST method is significantly outperformed by the rest of the proposed strategies, a fact that supports the effectiveness of our fine-tuning speaker adaptation approaches. * When the training set was used, the MST-based model provided a considerable quality of speech recognition. This result could mean that the end-to-end architecture employed in our experiments was able to generalise common patterns across speakers when addressing VSR. * Regarding the amount of data used during the fine-tuning process, the results reflect a drastic deterioration of system performance when the development set was used. However, this deterioration was noticeably lower when the TS-SAT strategy was applied, showing that this approach could be more robust against situations where a given speaker presents data scarcity. * The TS-SAT strategy stands as the best option when addressing speaker adaptation. This fact supports the idea that a two-step fine-tuning process in which the model is first adapted to the general task could benefit the final adaptation of the VSR system to a specific speaker. Results comparable to the current state of the art were obtained. Moreover, our findings suggest that the fine-tuning method employed in our experiments is capable of adapting VSR end-to-end architectures in a small number of epochs, even when only a limited amount of data is available. In addition, the average performance of the VSR system without fine-tuning (i.e., using the weights published by [PERSON] et al. [22] for the Spanish language) provided around 78% WER on the twenty speakers considered in our experiments. For reference, it should be noted that when using the entire nine-hour training set from the LIP-RTVE database, about 60% WER was obtained for the speaker-independent scenario. However, as mentioned in Section 2, the current state of the art in the field is around 25-30% WER for the English LRS3-TED database [19], a challenging speaker-independent database widely studied in the field [11; 21; 22]. Therefore, although we were able to obtain recognition rates of considerable quality, all these aspects suggest that further research should be considered. In addition, considering only those experiments where the training set was used, we evaluated each strategy for each of the speakers selected in our study, as shown in Figure 4. Similar conclusions to those mentioned above can be inferred from these results. Nonetheless, it is noteworthy that regardless of the strategy being applied, the VSR system provides remarkably different recognition rates depending on the speaker evaluated. Hence, a study was conducted to find reasons that could explain this behaviour. Several statistics were computed for each of these speakers, such as the number of words per utterance, the perplexity of the LM in the test samples, and the number of training seconds. Then, we analysed how each of the statistics varied as the word error rate increased. However, we were not able to identify any trends or patterns from these data. Therefore, we can say that these experiments suggest that the reason why VSR systems behaved in this way could be related to aspects that are difficult to model, such as better vocalizations or certain oral physiognnies that reflect more adequate speech articulations.