The growing practice of remote simultaneous interpreting (RSI) via cloud-based digital platforms, which has gained momentum during the COVID-19 pandemic, has opened up new opportunities for interpreters to provide simultaneous interpreting (SI) services. While under-researched, cloud-based RSI may have similar implications to the ones associated with booth-based RSI. Research has shown that booth-based RSI can be more tiring and stressful than conventional simultaneous interpreting and that it is more demanding in terms of information processing and mental modelling (Moser-Mercer, 2003/2005), with perceived negative implications for interpreting quality (Roziner and Shlesinger, 2010). Bringing together research on RSI and on the use of automatic speech recognition (ASR) as a computer-assisted interpreting (CAI) tool, which has been advocated as a means to support interpreters in different SI configurations (e.g., Defrancq and Fantinuoli, 2020), this study investigates the application of full ASR-generated transcripts in cloud-based RSI settings, examining their effect on interpreters’ output quality and overall user experience. As part of the experimental design, 16 professional conference interpreters performed a controlled interpreting test consisting of a warmup speech (not included in the analysis), and four speeches, i.e., two lexically dense speeches (LD) and two fast speeches (F), presented with and without ASR support. With regards to interpreting quality, which was measured by the NTR Model (Romero-Fresco and Pöchhacker, 2017), a system originally devised for assessing the accuracy of interlingual respeaking performance, results show higher average NTR scores across both speech types when ASR support is available. However, while LD speeches benefit statistically from ASR, F speeches show less improvement. Besides, the application of ASR-generated live transcripts seems to entail a loss in stylistic quality. Interpreters’ experiences vary, with most participants using ASR transcripts randomly or when facing difficulties and having different opinions regarding their experiences and the integration of ASR in cloud-based RSI. The study recommends exploring the use of ASR as a CAI tool with different speech types and ASR displays, its cognitive implications, and the role of CAI training, in order to inform best practices for effective ASR integration in cloud-based RSI.