# ODIN112-AI-Assisted Emergency Services in Romania
## 2 Related Work
### Automatic Speech Recognition
#### 2.1.2 Hidden Markov Model-based Architectures  
Hidden Markov Models (HMMs; [PERSON] and [PERSON] [5]) are a statistical Markov model in which the states are not observable. HMMs learn about the initial Markov process by observing a different process whose behavior depends on the modeled system. Gaussian Mixture Models (GMMs; [PERSON] [6]) are a probabilistic model that assumes all data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters.  
In speech recognition, HMMs are used together with GMMs. The purpose of the Hidden Markov Model is to consider the temporal dependencies between the acoustic features which represent the context. In contrast, the Gaussian Mixture Model is used to classify each frame, which represents the uttered phonemes. Paired with a pronunciation lexicon and a language model, HMM-GMM models (Figure 1) can transform a given sound wave into a word sequence. Each audio frame of the input wave is processed while acoustic features (for example, Mel-Frequency Cepstral Coefficients or MFCC for short) are extracted. The sentences are split into words, and then each word is converted into a  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Data Set** & **Duration** & **Recordings** & \
\begin{tabular}{c} **Unique** \\ **Transcripts** \\ \end{tabular} &
\begin{tabular}{c} **Unique Word** \\ **Count** \\ \end{tabular} & **Speakers** \\ \hline Chamber of Deputies (eval) & 4 h & 296 & 296 & 7084 & N/A \\ IIT & 18 h & 8877 & 8619 & 30,327 & 98 \\ RACAI & 11 h & 3404 & 2646 & 15,877 & 3 \\ RADOR & 50 h & 16,180 & 15,530 & 50,782 & N/A \\ RASC & 4 h & 2976 & 2972 & 14,113 & N/A \\ Robin & 1 h & 400 & 194 & 2165 & 4 \\ RoDigits & 37 h & 15,389 & 100 & 10 & 154 \\ Romanian Read-Speech [4] & 99 h & 136,120 & 11,924 & 18,485 & 164 \\ SSC (eval) & 5 h & 3135 & 3008 & 11,291 & N/A \\ SWARA & 21 h & 19,292 & 1803 & 6102 & 17 \\ Various & 32 h & 18,568 & 15,419 & 36,174 & 3444+ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Available speech datasets for the Romanian language.  
sequence of phonemes using a phonetic dictionary or an additional grapheme-to-phoneme model to determine the phonemes automatically.  
CMUSphinx [7] is the most well-known speech-to-text solution, a speech recognition development toolkit heavily used for experimenting with HMM-GMM-based speech recognition architectures (Figure 2). It supports multiple types of acoustic models: continuous, semicontinuous, and phonetic-tied models. The best results obtained with CMUSphinx for the English language were obtained after training with LibriSpeech (100 h of clean audio) ([[https://abucts.blogspot.com/2017/08/gsoc-2017-with-cmusphinx-post-11.html](https://abucts.blogspot.com/2017/08/gsoc-2017-with-cmusphinx-post-11.html)]([https://abucts.blogspot.com/2017/08/gsoc-2017-with-cmusphinx-post-11.html](https://abucts.blogspot.com/2017/08/gsoc-2017-with-cmusphinx-post-11.html)), accessed on 1 December 2022): 19.4% WER (continuous model) and 30.3% WER (phonetically tied model). The best results for the Romanian language vary between 3.8% WER and 23.17% WER, the former result being obtained [8] for a subset of a small and clean dataset, while the latter for a much more difficult evaluation dataset.