# ODIN112-AI-Assisted Emergency Services in Romania
## 3 Method  
### Corpus  
One advantage of using deep neural networks is that they can learn from much larger datasets. Many modern systems have been developed thanks not only to the advances in neural architectures but also because of the availability of data, storage capabilities, and the increased processing power of new computer hardware.  
The largest limitation in training accurate speech processing models for the Romanian language is the lack of data. Romanian is far from being a popular language and it is spoken by approximately 23 million people. The total duration of publicly available resources is approximately 300 h, out of which just about half are high quality and can be used for producing relevant models. As a comparison, much more popular languages such as English or Chinese have over 1000 h of labeled speech data and considerably more unlabeled speech datasets. A recent neural network model named Whisper [12] has been trained using 438,218 h of English hours and over 117,113 h, among the most popular languages being Chinese (23,446 h), German (13,344 h), Spanish (11,100 h), Russian (9761 h), French (9752 h), Portuguese (8573 h), Korean (7993 h), and Japanese (7054 h).  
Data Acquisition  
Datasets are one of the most important aspects of machine learning, and currently, audio datasets of hundreds of hours of labeled speech are usual. New models have been trained with over hundreds of thousands of hours of recordings. One remaining problem consists of finding or collecting high-quality training data (clear recordings and correct transcripts). In general, high-quality training datasets must be manually curated and are difficult and expensive to produce because of the amount of necessary time involved in recording and labeling data.  
New methods that make use of unsupervised learning or semisupervised learning can also include unlabeled entries. Still, in these cases, the data quantity requirements increase significantly, and data acquisition remains a tiresome task. Another idea is to use existent labeled data for bootstrapping a model that can transcribe the unlabeled data. Nevertheless, this method is not ideal either because it is subject to recognition errors.  
At this moment, there are several initiatives to crowdsource a dataset for the Romanian language. Two of the most successful examples are Mozilla Common Voice ([[https://commonvoice.mozilla.org](https://commonvoice.mozilla.org)]([https://commonvoice.mozilla.org](https://commonvoice.mozilla.org)), accessed on 1 December 2022) (40 h) and Echo ([[https://echo.readerbench.com](https://echo.readerbench.com)]([https://echo.readerbench.com](https://echo.readerbench.com)), accessed on 1 December 2022) (our project with more than 150 h, see Table 2).  
The data was recorded with the help of more than 200 volunteers, most of whom were students at the University Politehnica of Bucharest aged between 20 and 24 years. Tables 3 and 4 depict the distributions of their ages and gender; however, only about half of the volunteers disclosed this information.  
Data augmentation techniques are a common solution when the data is sparse. One such solution is SpecAugment [32], one of the most popular and efficient augmentation methods for speech. SpecAugment processes audio data by altering the audio spectrogram instead of the raw input audio waveform. This method is simple as it consists only of a set of basic operations: warping, masking of blocks in the frequency domain, or masking of time steps).