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Automatic speech scoring is crucial in language learning, providing targeted feedback to language learners by assessing pronunciation, fluency, and other speech qualities. However, the scarcity of human-labeled data for languages beyond English poses a significant challenge in developing such systems. In this work, we propose a Language-Independent scoring approach to evaluate speech without relying on labeled data in the target language. We introduce a multilingual speech scoring system that leverages representations from the wav2vec 2.0 XLSR model and a force-alignment technique based on CTC-Segmentation to construct speech features. These features are used to train a machine learning model to predict pronunciation and fluency scores. We demonstrate the potential of our method by predicting expert ratings on a speech dataset spanning five languages - English, French, Spanish, German and Portuguese, and comparing its performance against Language-Specific models trained individually on each language, as well as a jointly-trained model on all languages. Results indicate that our approach shows promise as an initial step towards a universal language independent speech scoring.