Past　tongue-jaw　movement　interaction　systems　typically　require　dedicated　hardware　and　are　uncomfortable　to　use,　limiting　their　scalability　and　generalizability.　This　paper　introduces　CanalScan,　the　first　system　that　recognizes　tongue-jaw　movements　using　commodity　speakers　and　microphones　mounted　on　ubiquitous　off-the-shelf　devices　(e.g.,　smartphones).　What　inspires　us　is　that　tongue-jaw　movements　always　cause　ear　canal　deformations,　and　we　find　that　for　different　tongue-jaw　movements,　dynamic　features　of　ear　canal　deformations　present　unique　patterns　on　acoustic　reflections　in　the　ear　canal.　Specifically,　CanalScan　first　sends　an　acoustic　signal　to　the　ear　canal,　then　parses　the　reflection　signals　for　tongue-jaw　movements　recognition.　To　eliminate　the　impacts　of　body　movements,　we　develop　a　body　movement　noise　filtering　method　and　a　dynamic　segmentation　method　to　identify　and　separate　the　tongue-jaw　movements-associated　ear　canal　deformations　from　other　types　of　body　movements.　We　further　propose　a　sensor　position　detection　method　and　a　data　transformation　mechanism　to　reduce　the　impacts　of　diversities　in-ear　canal　shapes　and　relative　positions　between　sensors　and　the　ear　canal.　CanalScan　explores　twelve　unique　and　consistent　features　and　applies　a　random　forest　classifier　to　distinguish　tongue-jaw　movements.　Extensive　experiments　with　twenty　participants　validate　the　generalizability,　effectiveness,　robustness,　and　high　accuracy　of　CanalScan.