  Speaker adaptation is important to build robust automatic speech recognition
(ASR) systems. In this work, we investigate various methods for speaker
adaptive training (SAT) based on feature-space approaches for a conformer-based
acoustic model (AM) on the Switchboard 300h dataset. We propose a method,
called Weighted-Simple-Add, which adds weighted speaker information vectors to
the input of the multi-head self-attention module of the conformer AM. Using
this method for SAT, we achieve 3.5% and 4.5% relative improvement in terms of
WER on the CallHome part of Hub5'00 and Hub5'01 respectively. Moreover, we
build on top of our previous work where we proposed a novel and competitive
training recipe for a conformer-based hybrid AM. We extend and improve this
recipe where we achieve 11% relative improvement in terms of word-error-rate
(WER) on Switchboard 300h Hub5'00 dataset. We also make this recipe efficient
by reducing the total number of parameters by 34% relative.
