Tone　is　one　of　the　biggest　differences　between　Mandarin　as　a　tonal　language　and　English,　meaning　that　changes　in　frequency　will　appear　in　every　character　and　determine　their　meaning.　Therefore,　the　accurate　identification　of　intonation　plays　an　important　role　in　Chinese　language　processing　and　learning.　In　the　learning　and　training　process　of　Mandarin　Chinese,　graphical　feedback　can　provide　valuable　assistance　to　linguists　or　learners,　providing　them　with　more　insight　than　simple　accuracy　ratings　as　they　can　determined　what　to　change　to　improve　their　intonation　based　on　this　feedback.　In　this　paper,　we　refer　to　the　process　of　generating　Mel-frequency　cepstrum　coefficients　and　employ　a　machine　learning-aided　time-slot　filtering　approach　to　obtain　an　intuitive　transformation　of　pitch　from　Mel　spectrum　to　geometric　graphs　as　feedback　to　Chinese　language　workers　and　learners,　and　to　help　them　improve　their　mastery　of　Mandarin　tones.　Our　preliminary　results　show　that　these　illustrations　can　accurately　represent　frequency　changes　in　relation　to　time.　At　the　same　time,　the　lower　computational　cost　of　the　optimization　allows　it　to　be　used　as　learning　software　on　various　mobile　platforms.　©　Published　under　licence　by　IOP　Publishing　Ltd.