# Under-Representated Speech Dataset from Open Data: Case Study on the Romanian Language
## 5 Dataset
\begin{table}
\begin{tabular}{c c c c c} \hline
**Indicator** & **Category** & **Duration** & **\# Segments** &
\begin{tabular}{c} **Avg. Seg.** \\ **Duration (s)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Gender} & F & 2 h 8 m 42 s & 1506 & 5.13 \\  & M & 2 h 10 m 13 s & 1131 & 6.91 \\ \hline \multirow{6}{*}{Age} & \(<\)14 & 10 m 44 s & 175 & 3.68 \\  & 14–19 & 15 m 20 s & 168 & 5.48 \\  & 19–29 & 1 h 5 m 34 s & 676 & 5.82 \\  & 30–50 & 45 m 41 s & 457 & 6.00 \\  & 50–70 & 1 h 1 m 22 s & 674 & 5.46 \\  & \(>\)70 & 1 h 0 m 14 s & 487 & 7.42 \\ \hline \multirow{3}{*}{MOS} & 5 & 2 h 20 m 34 s & 1187 & 7.11 \\  & 4 & 1 h 56 m 44s & 1435 & 4.88 \\  & 3 & 1 m 37 s & 15 & 6.47 \\ \hline \multirow{3}{*}{Platform} & YouTube & 3 h 32 m 48 s & 2014 & 6.34 \\  & Vimeo & 13 m 53 s & 194 & 4.29 \\  & SoundCloud & 32 m 14 s & 429 & 4.51 \\ \hline \multirow{3}{*}{License} & CC BY & 3 h 44 m 9 s & 2169 & 6.2 \\  & CC BY NC & 2 m 32 s & 39 & 3.9 \\  & CC BY NC SA & 32 m 14 s & 429 & 4.51 \\ \hline \multirow{3}{*}{Type} & Read & 33 m 32 s & 467 & 4.31 \\  & Spontaneous & 3 h 45 m 23 s & 2170 & 6.23 \\ \hline \end{tabular}
\end{table} 
Table 3: Dataset statistics. The text files holding the transcriptions are UTF-8 encoded, with the appropriate Romanian characters. Punctuation is available as entered by the dataset authors in the transcription process. The files are further annotated for lemma, part-of-speech, and dependency parsing, using UDPipe 1.3 [24], integrated in the RELATE platform ([[https://relate.racairo](https://relate.racairo)]([https://relate.racairo](https://relate.racairo))) [25; 26; 27], with a recent model [28]. We use the RELATE-integrated UDPipe text processing engine with the latest model because it strikes a good balance between the annotation accuracy and processing speed. As we show in [28], UDPipe is competitive (see Table 3 of the cited paper) with other text processing engines that are trained for Romanian (such as NLP-Cube [29], RNNTagger [30], and Stanza [31]), specifically in terms of accuracy when processing out-of-domain corpora, as our dataset does, when compared to the training data of UDPipe. The annotations are available in separate CoNLL-U Plus ([[https://universaldependencies.org/ext-format.html](https://universaldependencies.org/ext-format.html)]([https://universaldependencies.org/ext-format.html](https://universaldependencies.org/ext-format.html)), accessed on 3 October 2024) files, associated with the raw text files. Statistics on the text part are computed in the RELATE platform and are given in Table 5. Transcriptions are segmented in locations natural from the speech point of view. This results in text files containing multiple sentences (an average of 2.5 sentences in each file, with an average sentence length of 7.3 tokens). A large number of words appear only once in the corpus (hapax legomena), indicating the need for an externally trained language model if the ASR system makes use of this feature. With regard to part of speech tags, common nouns are the most represented, followed by verbs. Figure 1: Dataset statistics(as duration percentage of the entire dataset). Figure 2: Percentage of ages represented in the corpus, by gender M (outer doughnut), and F (inner doughnut). A number of proper nouns are also included, potentially making the recognition more challenging in the case of systems employing pre-trained language models. The dataset is publicly released on the Zenodo platform ([[https://doi.org/10.5281/ze](https://doi.org/10.5281/ze)]([https://doi.org/10.5281/ze](https://doi.org/10.5281/ze)) nodo.7898232). It is available under an open license, Creative Commons Attribution Non Commercial Share Alike (CC BY NC SA). The original content is available under its own license, as indicated in the metadata.