  We introduce an approach to identifying speaker names in dialogue
transcripts, a crucial task for enhancing content accessibility and
searchability in digital media archives. Despite the advancements in speech
recognition, the task of text-based speaker identification (SpeakerID) has
received limited attention, lacking large-scale, diverse datasets for effective
model training. Addressing these gaps, we present a novel, large-scale dataset
derived from the MediaSum corpus, encompassing transcripts from a wide range of
media sources. We propose novel transformer-based models tailored for
SpeakerID, leveraging contextual cues within dialogues to accurately attribute
speaker names. Through extensive experiments, our best model achieves a great
precision of 80.3\%, setting a new benchmark for SpeakerID. The data and code
are publicly available here:
\url{https://github.com/adobe-research/speaker-identification}
