# Under-Representated Speech Dataset from Open Data: Case Study on the Romanian Language
## 7 Conclusions  
The word error rate (WER) of current Romanian ASR systems is high on the USPDATRO dataset, which further means that such datasets need to be developed in order to bring the ASR WER down for under-represented categories of speakers. However, it is clear that for whatever language is in focus, under-represented categories of speech data need to be added to training datasets. With 4 h, 18 min and 55 s of speech in total but with an even split between men and women and with significantly more speech outside the 19-29 years of age category, the USPDATRO dataset is an example of a speech dataset specifically targeted at under-represented categories of speakers. The precise composition of the USPDATRO dataset was created by manually assembling short samples of speech that were available through online audio/video platforms (such as YouTube or Vimeo) that offered filtering by license. We were thus able to create a freely accessible speech corpus, containing speech samples from men and women, either very young or middle-aged or older. The corpus creation technique described in this paper can be easily reproduced for any language of interest, owing to the fact that speech data with open access licenses is available online. Besides investigating a new source of speech data, the USPDATRO project aimed to contribute towards increasing the Digital Language Equality Metric with regard to the Romanian language, by providing a new resource as well as an opportunity for building even larger speech resources. Collection of the USPDATRO dataset focused only on certain types of under-represented Romanian language speech. Future research may involve extending the dataset with content and enhancing it based on other characteristics of under-represented voices, such as dialects or non-Romanians speaking the Romanian language. Our approach can prove useful for creating speech datasets for other low-resourced languages. Furthermore, even though our work focused on the speech recognition task (ASR), gathering more under-represented speech may enhance the capabilities of text to speech (TTS) synthesis models \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Param**} & \multirow{2}{*}{**Beam**} & \multicolumn{2}{c}{**CoRoLa**} & \multicolumn{2}{c}{**USPDATRO**} \\  & & & **WER** & **CER** & **WER** & **CER** \\ \hline tiny & \multirow{2}{*}{39M} & N & 1.2218 & 0.8135 & 1.1502 & 0.6169 \\ tiny & & Y & 0.7903 & 0.3439 & 0.9115 & 0.4771 \\ base & & N & 0.6079 & 0.2534 & 0.9347 & 0.5086 \\ base & & Y & 0.6433 & 0.2625 & 0.7275 & 0.3391 \\ small & & N & 0.5027 & 0.2144 & 0.6789 & 0.3169 \\ small & & Y & 0.5005 & 0.2143 & 0.5794 & 0.2380 \\ medium & & N & 0.5562 & 0.2768 & 0.5566 & 0.2516 \\ medium & & Y & 0.4347 & 0.1887 & 0.5051 & 0.2064 \\ large-v2 & & N & 0.4561 & 0.2142 & 0.5104 & 0.2189 \\ large-v2 & & Y & **0.4052** & **0.1777** & **0.4874** & **0.1952** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation of Whisper models without Romanian language fine-tuning. for such voices. While exploiting social media platforms, such as YouTube, other platform-specific criteria can be used for selecting or categorizing the content of future corpora, such as audience, engagement, and categories. CoRoLa is the open-access national reference corpus of contemporary Romanian and includes both textual and oral data. While the textual part is highly used in many national and international projects, the oral part is under-exploited, and our explanation is that unlike the textual part which is classified and documented along multiple parameters, the oral part is stored with limited metadata. The work reported in this paper aimed at remedying this deficiency. The current oral data and all its multivalued metadata will be added to CoRoLa. The collecting of diversified speech data will continue, as there is still a need for special groups of speakers with respect to age, speech disabilities, and regional dialects.