# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
## 2 Literature Review
### ASR in the Portuguese Language  
The Portuguese language is one of the most spoken in the world, not due to the size of the Portuguese population (10 million), which gives the language its name, but thanks to countries with a much larger number of inhabitants, such as Brazil (214 million), Angola (34 million), and Mozambique (32 million). Despite speaking the same language, the speech varies from country to country and even from region to region, not only in accent but also in vocabulary. The goal of this work is to use European Portuguese (EP), i.e., from Portugal, to develop an ASR system.  
As already mentioned, EP has fewer speakers than other variants such as Brazilian Portuguese (BP). However, some research has already been developed in the field of ASR with the goal of transcribing EP speech. [PERSON] et al. [17] and [PERSON] et al. [18] aimed to transcribe speech from elder and young people since in these age groups people have more difficulties expressing themselves. The goal was to improve the understatement of their speech through the use of ASR systems. Other research aimed to create a speech recognizer for EP based on a corpus obtained from broadcast news and newspapers. The AUDIMUS.media [19] speech recognizer is a hybrid system composed of an ANN, a multilayer perceptron (MLP), which classifies phones according to features extracted by a Perceptual Linear Prediction (PLP), a log-RelActiveSpecTrAl (Log-RASTA), and Modulation Spectrogram (MSG). These components are then combined and used in an HMM for temporal modeling [20].  
In variants with a larger amount of speakers, such as Brazilian Portuguese, there is also a lack of results related to the development of ASR systems. This shortage is mostly due to the lack of data quantity, quality, or detail in public datasets, or lack of public datasets to begin with, though they are desperately needed, especially when creating models based on DNNs.  
[PERSON] et al. [21] provided a list of 24 Portuguese language datasets alongside some of the available features, such as size, quality, rate, amount of speakers, speaker's age, and availability (public or private). Of the twenty-four datasets, only six are public, which leads [PERSON] et al. to state that the amount of datasets available is acceptable to build ASR systems for the Portuguese language. [PERSON] et al. also conclude that the types of data are diverse (noisy, wide age range, medical, commands), but the overall quantity, quality, and standardization are poor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **MCV WER** & **Voxforge WER** \\ \hline Facebook wav2 vec 2.0 & 12.29\% & 11.44\% \\ Google LAS & 12.58\% & 10.49\% \\ Microsoft ASR System & 9.56\% & 7.25\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: APIs results on Mozilla Common Voice (MCV) Corpus and Voxforge Corpus datasets.  
Nevertheless, some research has shown that it is possible to create models for ASR systems for the Portuguese language using reduced amounts of data, as little as 1 h, and still achieve considerable results in word error rate (WER), as high as 34% [22]. Works regarding ASR systems for Portuguese using DNNs worth mentioning include [PERSON] et al. [23], who make use of Wav2 vec 2.0 and pre-trained models in other languages (which are then fine-tuned to BP) and achieve an average WER of 12.4% on seven datasets; and [PERSON] et al. [24; 25], who make use of four datasets (three of which are open) and use models based on DeepSpeech 2 [26] with convolutional and bidirectional recurrent layers, making it possible to achieve values of 25.45% WER. Additional work is available regarding ASR systems for the Portuguese language. The app TipTopTalk! [27] by [PERSON] et al. uses Google's ASR systems to implement a pronunciation training application for various languages including both European and Brazilian variants of Portuguese.