Although　the　automatic　speech　recognition　(ASR)　technology　is　increasingly　used　for　commercial　purposes,　its　impact　on　language　learning　has　not　been　extensively　studied.　Underpinned　by　the　sociocultural　theory,　the　present　work　examined　the　effects　of　leveraging　ASR　technology　to　support　English　vocabulary　learning　in　a　tertiary　flipped　setting.　A　control　group　and　an　experimental　group　of　college　students　participated　in　a　14-week　study.　Both　groups　had　their　English　classes　in　a　flipped　fashion,　but　the　experimental　group　was　assigned　with　ASR-assisted　oral　tasks　for　pre-class　self-learning.　The　pre-　and　post-intervention　in-class　task　performance　of　both　groups　was　audio-recorded　and　transcribed　for　data　analysis.　The　triadic　complexity-accuracy-fluency　(CAF)　framework　was　adopted　to　evaluate　the　participants＇　vocabulary　learning.　The　between-　and　within-subjects　effects　were　examined　mainly　through　procedures　of　MANCOVA　and　mixed-design　repeated　measures　ANCOVA.　Results　showed　that　on　all　the　metrics　of　lexical　complexity　and　speed　fluency,　the　experimental　group　outperformed　the　control　group,　and　had　significant　growth　over　time.　On　the　other　hand,　the　control　group　only　improved　significantly　overtime　on　the　G-index.　On　lexical　accuracy,　there　was　no　significant　difference　between　the　two　groups,　and　the　within-subjects　effect　was　not　significant　for　either　group.　The　findings　lent　some　support　to　Skehan＇s　Trade-off　Hypothesis　and　discussions　were　conducted　regarding　the　triarchic　CAF　framework.