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. © 2020 IEEE.