Self Attentive Context dependent Speaker Embedding for Speaker Verification

Sankala, Sreekanth and Mohammad Rafi, B. Shaik and Kodukula, Sri Rama Murty (2020) Self Attentive Context dependent Speaker Embedding for Speaker Verification. In: National Conference on Communications (NCC), 21-23 February 2020, Kharagpur, India.

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In the recent past, Deep neural networks became the most successful approach to extract the speaker embeddings. Among the existing methods, the x-vector system, that extracts a fixed dimensional representation from varying length speech signal, became the most successful approach. Later the performance of the x-vector system improved by explicitly modeling the phonological variations in it i.e, c-vector. Although the c-vector framework utilizes the phonological variations in the speaker embedding extraction process, it is giving equal attention to all the frames using the stats pooling layer. Motivated by the subjective analysis of the importance of nasals, vowels, and semivowels for speaker recognition, we extend the work of the c-vector system by including a multi-head self-attention mechanism. In comparison with the earlier subjective analysis on the importance of different phonetic units for speaker recognition, we also analyzed the attentions learnt by the network using TIMIT data. To examine the effectiveness of the proposed approach, we evaluate the performance of the proposed system on the NIST SRE10 database and get a relative improvement of 18.19 % with respect to the c-vector system on the short-duration case.

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IITH Creators:
IITH CreatorsORCiD
Kodukula, Sri Rama Murty
Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN: 978-172815120-5
Uncontrolled Keywords: Multi-head Self attention, Phonetic vector, Speaker recognition, Time-delay neural networks, X-vector
Subjects: Materials Engineering > Testing and measurement
Materials Engineering > Materials engineering
Materials Engineering > Nanostructured materials, porous materials
Materials Engineering > Organic materials
Materials Engineering > Composite materials
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 24 May 2021 07:33
Last Modified: 24 May 2021 07:33
Publisher URL:
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