Language is arguably what makes us unique as humans. We use it almost every waking minute, and in using it we convey and perceive multiple types of information. These include information about the meaning of an utterance (i.e. its 'regular' semantics) and information about attributes of the speaker who produced it, like their age and gender. A co-researcher, my adviser, and I set out to examine whether and how information about speaker attributes affects the learning of an artificial language that we created. For this study we recorded men and women saying words from our artificial language. Our language was constructed so that some sound sequences were only produced by women in the artificial language (like the shk sequence in the word bishker) and others were only produced by men (like the shp sequence in the word nishpam). Some sequences weren't presented at all (like the shm sequence). We constructed a task in which our research participants could implicitly learn this artificial language. We then tested whether the participants learned which sound sequences could occur in the novel language. They could: words with the shm sequence were perceived as poorer additions to our language than were untrained words with the shp and shk in them. However, listeners didn't learn the associations between sequences and genders. The knowledge gained from this study has applications in fields concerned with the study of language, such as speech-language-hearing science, second language acquisition, and linguistics, as well as related fields like Gender Studies.