## 3 Iterative Data Collection through Phonetic Decoding and Alignment  
For each Basque Parliament plenary session, an audio file and the corresponding minutes are available. In fact, for ease of processing, each audio file is manually split into two or three smaller chunks (each about 2 h long) and the minutes are split accordingly. As a starting point, a phone recognizer, trained on generic datasets for Basque and Spanish (not including BP materials) is applied to the audio files (without any phonological restrictions), to obtain a long sequence of phonetic units with their corresponding timestamps. Onthe other hand, the minutes are passed through the above mentioned G2P converter to acquire a reference (nominal) sequence of phonetic units. Finally, the recognized and reference sequences of phonetic units are aligned one with another under the criterion of maximizing the number of matching units (which is approximately the same as minimizing the number of deletions, insertions and substitutions), following the same text-and-speech alignment method that has been successfully applied in our group for the alignment of BP subtitles [28; 34; 35]. In this way, those regions showing a high density of errors in the alignment would correspond to parts of the minutes which do not match the audio contents. The recognized phonetic sequence sometimes features gaps between two consecutive units, which represent silent pauses. Gaps longer than 0.5 s are defined as potential _breaking points_. Then, a _slice_ is defined as an audio chunk between two consecutive breaking points and a _segment_ as an audio chunk comprising one or more consecutive slices. This means that a segment might contain one or more breaking points inside of it. Data collection is performed by searching for the segment lasting between 3 and 10 s with the highest phone recognition rate (PRR), defined as:  
\[\text{PRR}=100\cdot\frac{m}{m+d+i+s} \tag{1}\]  
where \(m\), \(d\), \(i\) and \(s\) are the number of matching units, deletions, insertions and substitutions yielded by the alignment for a given segment, respectively. When two or more segments attain the same (maximum) PRR, the longest segment is chosen. So PRR and length are the primary and secondary selection criteria, respectively. A single-pass search is performed (with linear time complexity) to maximize PRR and length over those segments meeting the duration constraints in an audio chunk \(U\). Note that, because of segment duration constraints, for each starting slice, the method has to consider just a limited number of following slices (usually one or two). Once determined the optimal segment \(s^{*}\) in an audio chunk \(U\), the two audio sub-chunks at the left and right sides of \(s^{*}\), \(U_{I}\) and \(U_{r}\), if not empty, are independently searched in two recursive calls (see Figure 1). Each call returns a list of segments, so we acquire two lists \(S_{U_{I}}\) and \(S_{U_{I}}\), which are merged along with the optimal segment \(s^{*}\) into a single list \(S_{U}\). In this way, after searching all the audio files, we end up with a list of segments \(S\) that can be filtered according to PRR. The recurrence relation defining the time complexity of the search procedure for an audio chunk \(U\) with \(n\) slices would be:  
\[T(n)=\left\{ \begin{array}{ll}1&n\leq 1\\ n+T(i)+T(j)&n>1\end{array} \right. \tag{2}\]  
where \(i\) and \(j\) (with \(i+j<n\)) are the number of slices in the audio chunks \(U_{I}\) and \(U_{r}\), respectively. Note that \(i=0\) implies that \(U_{I}\) would be empty and the recursive call would not be carried out; on the other hand, if \(i=1\), \(U_{I}\) would consist of a single slice and the search would reduce to checking duration constraints. The same stands for \(j\) and \(U_{r}\).