# Cascade or Direct Speech Translation? A Case Study +
## 3 The mintzai-ST Corpus
### Alignment and Filtering  
As a first step, metadata were filtered from the PDF-extracted text, and source and target files were extracted from the text in the original columns, preserving paragraph-level alignments. Speaker information was usually located at the beginning of a paragraph and was extracted automatically when available. Since sometimes speakers were identified by their function rather than their name, manual speaker identification was also performed (see Section 3.3 for details on speaker distribution in the final datasets). As a second step, language identification was performed on each paragraph. Although paragraphs were consistently in either one of the two languages overall, in some cases, language switching occurred within a given paragraph or within a sentence. Since any error identifying languages would propagate to subsequent processes, special attention was paid to ensuring correct language identification by employing two separate tools on the content: TextCat and the language identifier of the OpenNER project [45] (available at the following addresses, respectively: [[https://github.com/Trey314159/TextCat](https://github.com/Trey314159/TextCat)]([https://github.com/Trey314159/TextCat](https://github.com/Trey314159/TextCat)), accessed on 31 January 2017, and [[https://github.com/opener-project/language-identifier](https://github.com/opener-project/language-identifier)]([https://github.com/opener-project/language-identifier](https://github.com/opener-project/language-identifier)), accessed on 31 July 2014). Paragraphs were discarded if either tool produced different results as their topmost identified language or if neither tool identified either one of the expected languages; in all other cases, we retained the language identified as most probable. The third step involved forced alignment, where each word in the source transcription was aligned to a section of the corresponding audio file via source and time indications. This step was performed with the Kaldi toolkit [46], using an in-house bilingual model to reduce the impact of remaining language identification uncertainties. Forced alignment was performed with different beam sizes, to capture the largest possible number of alignments in a corpus where audio transcriptions were produced by human experts, and therefore assumed to be complete and correct for the most part. The forced alignment results are shown in Table 2.  
Overall, more recent years featured more literal transcriptions, requiring smaller beams to perform alignment. Increasingly larger beams were applied to preserve as much of the content as possible in this initial alignment step, under the assumption that transcriptions of the audio content had been professionally created and were thus correct for the most part. At this stage, the source and target files were split on the basis of the previous alignment information, with one paragraph per file. Forced alignment was then applied again, this time with a monolingual model and a small beam size of one, along with a retry beam of two, to discard alignment errors and non-literal transcriptions. This process resulted in the perfect and imperfect alignments reported in Table 3, with the former being kept for the final corpus and the latter discarded. \begin{table}
\begin{tabular}{c c c c c} \hline
**Year** & **Videos** & **PDF** & **Hours** & **Words** \\ \hline
2011 & 43 & 21 & 86.51 & 132,595 \\
2012 & 38 & 21 & 117.94 & 173,199 \\
2013 & 67 & 38 & 215.00 & 306,621 \\
2014 & 60 & 30 & 176.83 & 252,887 \\
2015 & 41 & 27 & 134.10 & 195,112 \\
2016 & 38 & 21 & 113.85 & 170,608 \\
2017 & 49 & 33 & 173.57 & 250,862 \\
2018 & 34 & 26 & 128.38 & 207,910 \\ \hline Total & 370 & 217 & 1146.18 & 18,625,252 \\ \hline \end{tabular}
\end{table} 
Table 1: Collected raw data statistics. Since translation models require specific sentence-based training bitexts, the previously aligned paragraphs were further prepared with sentence splitting, tokenisation, and truecasing.