Monaural Multi-Speaker Speech Separation Using Efficient Transformer Model
This work addresses the trade-off between model size and accuracy in speech separation, offering a computationally efficient solution for applications like hearing aids or voice assistants, though it appears incremental as it builds on existing Transformer methods.
The paper tackles the cocktail party problem by proposing a monaural multi-speaker speech separation model using an efficient Transformer architecture, trained on the LibriMix dataset to separate two distinct speakers from mixed audio with reduced computational complexity while maintaining performance.
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.