Comparison of modern and traditional Slovak children’s speech recognition

We compare two distinct speech recognition approaches, namely Hidden Markov models mixed with deep neural networks and modern end-to-end neural speech recognition architectures. Our evaluation focuses on the metrics of performance in the context of low-resource data. Here, we utilize newly completed Slovak speech recognition dataset containing children’s speech from Slovak public TV talk show intended for children. Our objective is to assess the feasibility of utilizing the Slovak children dataset within the framework of state-of-the-art end-to-end speech recognition, specifically by employing ESPnet2 framework. We anticipate the end-to-end model’s performance to be inferior in comparison with any tailored hybrid Hidden Markov model. The results from the baseline experiments motivate us to employ other advanced techniques that we propose to further enhance quality of models moving forward.