  Mispronunciation Detection and Diagnosis (MDD) systems, leveraging Automatic
Speech Recognition (ASR), face two main challenges in Mandarin Chinese: 1) The
two-stage models create an information gap between the phoneme or tone
classification stage and the MDD stage. 2) The scarcity of Mandarin MDD
datasets limits model training. In this paper, we introduce a stateless RNN-T
model for Mandarin MDD, utilizing HuBERT features with pitch embedding through
a Pitch Fusion Block. Our model, trained solely on native speaker data, shows a
3% improvement in Phone Error Rate and a 7% increase in False Acceptance Rate
over the state-of-the-art baseline in non-native scenarios
