  Cocktail party problem is the scenario where it is difficult to separate or
distinguish individual speaker from a mixed speech from several speakers. There
have been several researches going on in this field but the size and complexity
of the model is being traded off with the accuracy and robustness of speech
separation. "Monaural multi-speaker speech separation" presents a
speech-separation model based on the Transformer architecture and its efficient
forms. The model has been trained with the LibriMix dataset containing diverse
speakers' utterances. The model separates 2 distinct speaker sources from a
mixed audio input. The developed model approaches the reduction in
computational complexity of the speech separation model, with minimum tradeoff
with the performance of prevalent speech separation model and it has shown
significant movement towards that goal. This project foresees, a rise in
contribution towards the ongoing research in the field of speech separation
with computational efficiency at its core.
