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An i-Vector Extractor Suitable for Speaker Recognition with Both Microphone and Telephone Speech.
Graph Relational Features for Speaker Recognition and Mining.
Unsupervised Domain Adaptation for I-Vector Speaker Recognition.
Unsupervised Clustering Approaches for Domain Adaptation in Speaker Recognition Systems.
Limited Labels for Unlimited Data: Active Learning for Speaker Recognition.
The impact of Short Utterances in Speaker Recognition is of significant importance. Despite the advancements in short utterance speaker recognition (SUSR), text dependence and the role of phonemes in ...
Short Utterance Speaker Recognition (SUSR) is an important area of speaker recognition when only small amount of speech data is available for testing and training. We list the most commonly used state...
The length of the test speech greatly influences the performance of GMM-UBM based text-independent speaker recognition system, for example when the length of valid speech is as short a...
In the field of speaker recognition, short utterance speaker recognition (SUSR) has been attracting more and more attention in recent years. Despite the advancement in this technology and use ...
The task of speech processing is being spread over last years with speaker recognition, which consists of speaker identification and speaker verification. In the paper there is an explained method w...
Emotion is one of the important factors that cause the system performance degradation. By analyzing the similarity between channel effect and emotion effect on speaker recognition, an emotion compensa...
Speaker recognition over a public telephone network involves various types of transmission channels and handsets, which leads to mismatched channels (between the enrolled models and the test utterance...
Information of speech units like vowels, consonants and syllables can be a kind of knowledge used in text-independent Short Utterance Speaker Recognition (SUSR) in a similar way...

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