Speaker Recognition for Multi-speaker Conversations Using X-vectors

By David Snyder, Daniel Garcia-Romero, Gregory Sell, Alan McCree, Daniel Povey, Sanjeev Khudanpur. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019

322 citations recorded by the source corpus.

Recently, deep neural networks that map utterances to fixed-dimensional embeddings have emerged as the state-of-the-art in speaker recognition. Our prior work introduced x-vectors, an embedding that is very effective for both speaker recognition and diarization. This paper combines our previous work and applies it to the problem of speaker recognition on multi-speaker conversations. We measure performance on Speakers in the Wild and report what we believe are the best published error rates on this dataset. Moreover, we find that diarization substantially reduces error rate when there are multiple speakers, while maintaining excellent performance on single-speaker recordings. Finally, we introduce an easily implemented method to remove the domain-sensitive threshold typically used in the clustering stage of a diarization system. The proposed method is more robust to domain shifts, and achieves similar results to those obtained using a well-tuned threshold.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/da45697cbbf1c4891f37fc679a274a6ec4d2dcc0

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Citations in the corpus

ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification (2020, methodology): “The first baseline system is the Extended TDNN x-vector architecture [12, 5, 6] and improves upon the original x-vector system introduced in [1].”
End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors (2020, background): “Progress on better speaker embeddings, such as x-vectors [10, 11] and d-vectors [12, 13], have enabled accurate clusteringbased diarization.”
In defence of metric learning for speaker recognition (2020, methodology): “This weakness has been addressed by [11] who have proposed angular softmax (A-Softmax) where cosine similarity is used as logit input to the softmax layer, and a number of works have demonstrated its superiority over vanilla softmax in speaker recognition [6, 7, 8, 12, 13].”
The Idlab Voxsrc-20 Submission: Large Margin Fine-Tuning and Quality-Aware Score Calibration in DNN Based Speaker Verification (2020, result): “A recent shift towards neural network based speaker verification systems resulted in significantly better performance compared to the more traditional i-vector based systems [1, 2].”