Wavesplit: End-to-End Speech Separation by Speaker Clustering
Table 3: Error Analysis on WSJ0-2mix.
['Set', 'Metric', 'Δ SDR (dB) <5.0', 'Δ SDR (dB) >=5.0']
We examine further the accuracy on WSJ0-2mix. Our results show that the test set exhibits a larger fraction of sentences with <5 dB ΔSDR than the validation set, i.e. 7.2% versus 1.3%. The WSJ0-2mix validation set contains the same speakers as the training data, we therefore examine further if the lower test performance is due to the representation of the test speakers. We rely on the training speaker classifier from Eq. When the same training identity collects more than 10% of the probability mass in both clusters, we label the sequence as ”confusing speakers” since both clusters contains vectors confused with the same training identity. We observe that low SDR correlates with situations where both speakers in the test recordings are mapped to the same training identity. The training set of WSJ0-2mix has only 101 speakers. We suspect that training on datasets with a wider range of speakers will alleviate this problem and will be studied in future work.