With　the　rapid　expansion　of　the　we-media　industry,　streamers　have　increasingly　incorporated　inappropriate　content　into　live　videos　to　attract　traffic　and　pursue　interests.　Blacklisted　streamers　often　forge　their　identities　or　switch　platforms　to　continue　streaming,　causing　significant　harm　to　the　online　environment.　Consequently,　streamer　re-identification　(re-ID)　has　become　of　paramount　importance.　Streamer　biometrics　in　live　videos　exhibit　multimodal　characteristics,　including　voiceprints,　faces,　and　spatiotemporal　information,　which　complement　each　other.　Therefore,　we　propose　alight　cross-modal　attention　network　(LCMA-Net)　for　streamer　re-ID　in　live　videos.　First,　the　voiceprint,　face,　and　spatiotemporal　features　of　the　streamer　are　extracted　by　RawNetSA,　Pi-　Net,　and　STDA-ResNeXt3D,　respectively.　We　then　design　alight　cross-modal　pooling　attention　(LCMPA)　module,　which,　combined　with　a　multilayer　perceptron　(MLP),　aligns　and　concatenates　different　modality　features　into　multimodal　features　within　the　LCMA-Net.　Finally,　the　streamer　is　re-identified　by　measuring　the　similarity　between　these　multimodal　features.　Five　experiments　were　conducted　on　the　StreamerReID　dataset,　and　the　results　demonstrated　that　the　proposed　method　achieved　competitive　performance.　The　dataset　and　code　are　available　at　https://github.com/BJUT-AIVBD/LCMA-Net.