Recent　literature　advances　motion　sensors　mounted　on　smartphones　and　AR/VR　headsets　to　speech　eavesdropping　due　to　their　sensitivity　to　subtle　vibrations.　The　popularity　of　motion　sensors　in　earphones　has　fueled　a　rise　in　their　sampling　rate,　which　enables　various　enhanced　features.　This　paper　investigates　a　new　threat　of　eavesdropping　via　motion　sensors　of　earphones　by　developing　EarSpy,　which　builds　on　our　observation　that　the　earphone＇s　accelerometer　can　capture　bone　conduction　vibrations　(BCVs)　and　ear　canal　dynamic　motions　(ECDMs)　associated　with　speaking;　they　enable　EarSpy　to　derive　unique　information　about　the　wearer＇s　speech.　Leveraging　a　study　on　the　motion　sensor　measurements　captured　from　earphones,　EarSpy　gains　abilities　to　disentangle　the　wearer＇s　live　speech　from　interference　caused　by　body　motions　and　vibrations　generated　when　the　earphone＇s　speaker　plays　audio.　To　enable　user-independent　attacks,　EarSpy　involves　novel　efforts,　including　a　trajectory　instability　reduction　method　to　calibrate　the　waveform　of　ECDMs　and　a　data　augmentation　method　to　enrich　the　diversity　of　BCVs.　Moreover,　EarSpy　explores　effective　representations　from　BCVs　and　ECDMs,　and　develops　a　convolutional　neural　model　with　Connectionist　Temporal　Classification　(CTC)　to　realize　accurate　speech　recognition.　Extensive　experiments　involving　14　participants　demonstrate　that　EarSpy　reaches　a　promising　recognition　for　the　wearer＇s　speech.　©　2023　IEEE.