Multi-Task Based Mispronunciation Detection of Children Speech Using Multi-Lingual Information

In developing a Computer-Aided Pronunciation Training (CAPT) system for Chinese ESL (English as a Second Language) children, we suffered from insufficient task-specific data. To address this issue, we propose to utilize first language (L1) and second language (L2) knowledge from both adult and children data through multitask-based transfer learning according to Speech Learning Model (SLM). Experimental set-up includes the TDNN acoustic modelling using the following training data: 70 hours of English speech by American Children (AC), 100 hours by American Adults (AA), 5 hours of Chinese speech by Chinese Children (CC), and 89 hours by Chinese Adults (CA). Testing data includes 2 hours of ESL speech by Chinese children. Experimental results showed that the inclusion of AA data brought about 13% relative Detection Error Rate (DER) reduction compared to AC only. Further inclusion of CC and CA data through L1 transfer learning brought about a total of 21% relative improvement in DER. These results suggested the proposed method is effective in mitigating insufficient data problem.