# Cascade or Direct Speech Translation? A Case Study +
## 3 The mintzai-ST Corpus
### Alignment and Filtering
All operations were performed with the scripts from the [PERSON] toolkit [47]. Sentence-level alignments were then computed with the Hunalig toolkit [48], with an alignment probability of 0.4. Table 4 summarises the volumes of data after the alignment and filtering steps on speech data, as described above. The filtered and aligned data were then randomly split into training, dev, and test subsets of triplets consisting of audio, transcription, and translation. Triplets were removed from the test sets if the transcription-translation pair appeared in the training set as well. This measure was adopted to account for the fact that even minor acoustic differences might make a triple differ from another, even though the transcription-translation pair would be a duplicate for the machine translation component. Stricter removal along the previous lines allowed for a fair comparison between cascade and end-to-end models and made for a more difficult test set, as it mainly discarded acoustic variants of greetings and salutations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{4}{c}{**Alignment Beam**} \\ \cline{2-5}  & **10** & **100** & **1000** & **10,000** \\ \hline
2011 & 2 & 51 & 32 & 0 \\
2012 & 1 & 72 & 44 & 1 \\
2013 & 1 & 186 & 7 & 3 \\
2014 & 0 & 136 & 28 & 1 \\
2015 & 0 & 23 & 98 & 1 \\
2016 & 3 & 47 & 55 & 1 \\
2017 & 1 & 151 & 5 & 1 \\
2018 & 0 & 111 & 4 & 0 \\ \hline All & 8 & 776 & 273 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of aligned audio and transcription files via forced alignment with different beam sizes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Duration**} & \multicolumn{2}{c}{**Aligned**} & \multicolumn{2}{c}{**Discarded**} & \multirow{2}{*}{**\% Speech**} & \multirow{2}{*}{**\% Perfect**} \\ \cline{2-2} \cline{5-8}  & **(Hours)** & **PARA** & **Hours** & **PARA** & **Hours** & & \\ \hline
2011 & 86.51 & 9596 & 49.81 & 4572 & 31.75 & 94.26 & 61.07 \\
2012 & 117.94 & 13,991 & 71.49 & 5547 & 38.92 & 93.62 & 64.75 \\
2013 & 215.00 & 24,989 & 146.60 & 7788 & 55.20 & 93.86 & 72.65 \\
2014 & 176.83 & 19,523 & 113.44 & 7093 & 52.34 & 93.75 & 68.43 \\
2015 & 134.10 & 13,167 & 80.15 & 6004 & 46.13 & 94.17 & 63.47 \\
2016 & 113.85 & 11,418 & 65.78 & 5372 & 41.31 & 94.07 & 61.42 \\
2017 & 173.57 & 19,129 & 109.44 & 7474 & 54.77 & 94.60 & 66.65 \\
2018 & 128.38 & 16,525 & 84.69 & 5711 & 36.85 & 94.67 & 69.68 \\ \hline All & 1146.20 & 128,338 & 721.40 & 49,561 & 357.27 & 94.11 & 66.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Forced alignment results at the paragraph level. PARA indicates the number of paragraphs, % speech the percentage of identified speech in the audio, and % perfect the percentage of perfectly aligned content within identified speech. Table 5 shows the final statistics of the mintzai-ST corpus. With approximately 191 h for Basque (hereafter, EU) to Spanish (hereafter, ES) and 468 h for Spanish to Basque, the corpus is among the largest available ones for speech translation, featuring a language pair that differs significantly from the ones in publicly available corpora.