# End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning

## TL;DR

This paper introduces an end-to-end multi-speaker speech recognition system that leverages speaker embeddings and transfer learning to effectively transcribe overlapped speech without needing parallel non-overlapped data.

## Contribution

It proposes a novel framework combining speaker embeddings and transfer learning for multi-speaker ASR, independent of speaker count and non-overlapped speech data.

## Key findings

- Significant improvement in overlapped speech recognition accuracy
- Effective use of transfer learning from clean speech datasets
- No requirement for parallel non-overlapped speech materials

## Abstract

This paper presents our latest investigation on end-to-end automatic speech recognition (ASR) for overlapped speech. We propose to train an end-to-end system conditioned on speaker embeddings and further improved by transfer learning from clean speech. This proposed framework does not require any parallel non-overlapped speech materials and is independent of the number of speakers. Our experimental results on overlapped speech datasets show that joint conditioning on speaker embeddings and transfer learning significantly improves the ASR performance.