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End-to-end models are fast replacing conventional hybrid models in automatic speech recognition. A transformer is a sequence-to-sequence framework solely based on attention, that was initially applied to machine translation task. This end-toend framework has been shown to give promising results when used for automatic speech recognition as well. In this paper, we explore different ways of incorporating speaker information while training a transformer-based model to improve its performance. We present speaker information in the form of speaker embeddings for each of the speakers. Two broad categories of speaker embeddings are used: (i) fixed embeddings, and (ii) learned embeddings. We experiment using speaker embeddings learned along with the model training, as well as one-hot vectors and x-vectors. Using these different speaker embeddings, we obtain an average relative improvement of 1% to 3% in the token error rate. We report results on the NPTEL lecture database.NPTEL is an open-source e-learning portal providing content from top Indian universities.