Contextual Phonetic Pretraining for End-to-endUtterance-level Language and Speaker Recognition
Table 2: Fisher speaker recognition task results
['[BOLD] System', '[BOLD] EER', '[BOLD] minDCF08', '[BOLD] minDCF10']
The results of i-vector and x-vector from the original work are first presented. To ablate the merit of the SAP layer, we train a similar x-vector architecture with self-attentive instead of regular statistics pooling to get a 13.2% relative EER reduction. Training on contextual frame representations induced from the ASR model leads to better performance than the x-vector approach and improves performance even over the multitasking approach (where the phonetic extractor is learned jointly between ASR and SR)