# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
## 3 Data
### SpeechDat  
The SpeechDat European Project ([[https://cordis.europa.eu/project/id/LE24001](https://cordis.europa.eu/project/id/LE24001)]([https://cordis.europa.eu/project/id/LE24001](https://cordis.europa.eu/project/id/LE24001))) (accessed on 17 July 2022) was developed between 1996 and 1998 with the goal of providing speech resources to stimulate research and development of automated services, such as speech recognizers. The Portuguese database was collected by Portugal Telecom, now named Alice Portugal, in collaboration with INESC and INSECTEL.  
The current work was developed as a collaboration between Universidade de Evora and Alice Labs (Altice Labs is part of Alice Portugal), which enabled the current work to study ASR in Portuguese using the SpeechDat Portuguese dataset. SpeechDat audio recordings consist of 4027 Portugal Telecom employees following prompt sheets. This dataset has a good geographical distribution and a good representation of regional European Portuguese accents of native and non-native, as shown in Table 3.  
According to the documentation, speakers from \"Portuguese-speaking countries\" (which includes Macau and India, since there are speakers from these regions where there were Portuguese colonies and there exist Portuguese-speaking communities) have been living in Portugal for many years, so their dialectal differences are not so easily distinguishable. The speakers born in \"other countries\" are relatives of Portuguese emigrants.  
Table 4 displays the distribution of the speakers' sexes and age groups. The majority of the speakers belong to the 16-30 and 31-45 age groups. The female sex (\(\simeq\)54%) is slightly more represented than the male sex (\(\simeq\)46%).  
The audio recordings, consisting of small sentences, were encoded using A-LAW [31] (an algorithm used for encoding audio signals, in particular voice encoding) with a sampling rate of 8 kHz 8 bit, which was accompanied by an ASCII label file containing the audio recording metadata.  
Each label file contained an assessment code regarding the quality of the respective audio. The possible values for the code are the following:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Age Groups** & **Male Speakers** & **Female Speakers** & **Percentage of Total** \\ \hline \textless{}16 & 93 & 148 & 6.0 \\
16-30 & 581 & 823 & 34.9 \\
31-45 & 742 & 790 & 38.0 \\
46-60 & 376 & 335 & 17.7 \\ \textgreater{}60 & 69 & 70 & 3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Distribution of speakers over age groups and sexes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Region** & **Number of Speakers** & **Number of Speakers (\%)** \\ \hline Entre-Douro-e-Minho & 1048 & 26 \\ Transmontano & 202 & 5 \\ Beira-Litoral & 537 & 13 \\ Beira-Alta & 227 & 6 \\ Beira-Baixa & 97 & 2 \\ Estremadura & 858 & 21 \\ Ribatejo & 151 & 4 \\ Alentjeo & 325 & 8 \\ Algarve & 148 & 4 \\ Azores & 163 & 4 \\ Madeira & 95 & 2 \\ \hline Portuguese-speaking & & \\ countries & 116 & 3 \\ Other countries & 29 & 1 \\ Empty field & 31 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of native and non-native speakers.  
* OK--clean audio and ready to be used;
* NOISE--audio with some background noise;
* GARBAGE--empty audio, missing transcriptions, only background noise, noise produced by others;
* OTHER--audio containing disfluencies, hesitations, stuttering, or unintelligible speech;
* NO_PTO--audio files without label file.  
Table 5 presents the number of hours of audio recordings of each audio quality label and the total hours of audio recordings of the SpeechDat dataset. The 186 total hours of audio recordings represent a lexicon of approximately 15,000 different words, the smaller recordings being \(\simeq\)0.5 s and the larger recordings being \(\simeq\)20 s.  
We can observe that the SpeechDat dataset is slightly larger than the Multilingual LibriSpeech, by \(\simeq\)18 h.