###### Abstract  
Computer-based job interview training, including virtual reality (VR) simulations, have gained popularity in recent years to support and aid autistic individuals, who face significant challenges and barriers in finding and maintaining employment. Although popular, these training systems often fail to resemble the complexity and dynamism of the employment interview, as the dialogue management for the virtual conversation agent either relies on choosing from a menu of prespecified answers, or dialogue processing is based on keyword extraction from the transcribed speech of the interviewee, which depends on the interview script. We address this limitation through automated dialogue act classification via transfer learning. This allows for recognizing intent from user speech, independent of the domain of the interview. We also redress the lack of training data for a domain general job interview dialogue act classifier by providing an original dataset with responses to interview questions within a virtual job interview platform from 22 autistic participants. Participants' responses to a customized interview script were transcribed to act and annotated according to a custom 13-class dialogue act scheme. The best classifier was a fine-tuned bidirectional encoder representations from transformers (BERT) model, with an f1-score of 87%. dialogue act classification; transfer learning; autism spectrum disorder; virtual reality job interviews +
Footnote †: journal: _Signals_  
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to workplace environments (e.g., lighting, sounds, smells, etc.) [8], lack of support from vocational rehabilitation services or a lack of services thereof [9], and/or a poor job fit [8]. Amongst those challenges, the job interview, where autistic individuals are expected to adhere to the \"norm\" in terms of mannerisms and interactions, poses a major barrier to employment, and in turn, to independent living [7, 10, 11]. To address the above, several computer-based job interview simulators have been developed, catering to autistic individuals' needs (e.g., routine and certainty), which have demonstrated efficacy [12, 13, 14, 15, 16, 17]. VR-based interview training systems, including VR simulations, provide autistic job candidates a controlled self-paced learning environment to practice their interviewing skills across multiple scenarios with lower interpersonal risk and a sense of psychological safety [12, 18, 19]. Studies have shown that technology-enhanced job interview training platforms can improve interviewing skills and overall self-confidence when compared to traditional interview training methods [12, 20]. Further, virtual job interview training can potentially reduce costs, as it reduces the need for personnel to conduct real-life mock interviews [21]. Studies have demonstrated that depending on its realism, a virtual interview can induce experiences of anxiety or nervousness consistent with real interviews, which can help candidates get accustomed to the affective experience of interviews, and in turn, feel more comfortable during a real interview scenario [22, 23, 24]. Previous virtual job interview simulations with conversation agents, aimed at helping autistic individuals, collect interviewee (participant) responses and perform keyword extraction on transcribed text, which is dependent on the domain of the interview script [12, 14, 15, 16, 19]; oftentimes, the interview systems do not allow for freely spoken responses, and instead provide multiple answers from which to choose the appropriate one. Though the existing methods work, they are ad hoc solutions, and the interview systems lack text classification methods which can recognize the intent of the interviewee's spoken utterances, independent of the domain of the interview script. In our recent work [25], we show how manually identifying intents from autistic interviewees' utterances can provide insight into their performance.