## 5 Results  
This section presents the results of our work on the recognition of teaching activities of a lecturer from university class recordings. In the recordings of the lectures registered at our university prior to 2020, only the lecturer's voice was available. As commented before, our dataset was composed of 34 lecture recordings of different courses, such as mathematics, electronic devices, physical oceanography, statistics, etc. Out of these 34 recordings, the transcriptions of 14 of them were manually revised. In total, the recordings were approximately 3700 min long, of which 1600 min were lectures given by women and 2100 min by men. The model was evaluated using a partition of 90% for training and 10% for evaluation. We shuffled the dataset and stratified the partition, so the evaluation held approximately one tenth of the data of each class or teaching activity. As the dataset was imbalanced, a class weight tensor to account for this imbalance was computed. We report the values of precision, recall and F-score for each class in Table 7 and the confusion matrix of the results in Figure 10.  
In the confusion matrix of Figure 10, rows show the true label of the segments, i.e., the label we manually assigned to the segments, and columns represent the class predicted by our model. The values on the diagonal are the number of true positives (TP); for each class, the values in the columns show the false positives (FP) and the values in the rows show the number of false negatives (FN). The best performing class is Miscellaneous, with an F-score of 0.875, followed by Indistinct Chat, Exercise/Problem and Interaction with F-scores 0.437, 0.391 and 0.367, respectively. In the confusion matrix, the low values of the last row indicate that Miscellaneous is an easily distinguishable class, as our model correctly classifies the majority of Miscellaneous frames. However, a considerable amount of Pause frames are classified as Miscellaneous, as shown by the value 0.377 in the last column of Figure 10.  
Moreover, the results obtained for the Theory/Concept and Organization classes indicate that, although the values are worse than for the other classes, the model is able to distinguish Theory/Concept and Organization frames. Specifically, the Organization class has a distinctive vocabulary (dates, grading system, submitting exercises) that differentiates it from the other classes. Conversely, the metrics of the classes Digression, Other and Example/Real Application fall behind the rest of the classes. The reason for the poor performance of Digression and Other can be found in the low number of samples of these two classes in the dataset, as these activities occurred infrequently during the lectures, and their duration was usually rather short. In the fourth and fifth column of Figure 10, we can observe that our model hardly predicts frames as belonging to classes Digression and Other. Furthermore, frames of Digression are predicted as belonging to Interaction, while Other frames are misclassified as Organization, Interaction and Indistinct Chat. Furthermore, in the third column, it should be noted that our model is biased towards Exercise/Problem,and predicts a considerable proportion of Theory/Concept, Example/Real Application, Organization and Interaction as belonging to Exercise/Problem. Taking into account these results, it is worth noting that the best performing classes (Miscellaneous, Indistinct Chat, Exercise/Problem and Interaction), together with Pause, are the classes that were more readily distinguishable by means of the audio signal. This seems to indicate that the model is paying more attention to audio features than text features, as can be observed by the low value of the errors in the confusion matrix, particularly in the rows and columns of Miscellaneous, Indistinct Chat and Pause. A possible way to address the audio-skewed behavior of our model would be to modify the way in which the audio and text features are forwarded to the classifier. Another