Abstract: An AI-based approach to Garbled speech (GS) detection. Machine learning (ML) models are created that can distinguish between GS speech and non-GS speech with high accuracy. The machine learning models take as input an encoded speech frame that has passed a CRC check. The input data/predictors to the models are a selected set of information elements (IEs) (i.e., a set of one or more bits) of the encoded speech frame. The selected IEs are a part of the input parameters to the speech decoder. It is possible to operate on single encoded speech frames, in contrast to using decoded frames, which requires taking a previous encoded frame into account for being able to perform the decoding.