Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The great success of Minimum Phone Error (MPE) training criterion in mono-language large vocabulary continuous speech recognition (LVCSR) tasks motivates us to apply it to bilingual LVCSR systems. , in conjunction with the previous respectable bilingual phoneme inventory construction techniques,  give a comprehensive investigation to the performance of MPE/fMPE on various Mandarin-English bilingual test sets under different test conditions. The evaluation results show that the final fMPE+MPE model achieves significant improvements compared to the baseline models. On the mono-language test sets, the best improvement is a relative error rate reduction of 28.4%. And on the code-mixing test set, it also achieves a relative error rate reduction of 8.1%. The within- and cross-language substitution error rate introduced in this paper also explicitly shows that fMPE/MPE training can effectively improve the model's within- and cross-language discriminabilities in our bilingual recognition tasks.
['Ran Xu', 'Qingqing Zhang', 'Jielin Pan', 'Yonghong Yan']
I am writing a research paper on a idea Investigations to Minimum Phone Error Training in Bilingual Speech Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Investigations to Minimum Phone Error Training in Bilingual Speech Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The great success of Minimum Phone Error (MPE) training criterion in mono-language large vocabulary continuous speech recognition (LVCSR) tasks motivates us to apply it to bilingual LVCSR systems. , in conjunction with the previous respectable bilingual phoneme inventory construction techniques,  give a comprehensive investigation to the performance of MPE/fMPE on various Mandarin-English bilingual test sets under different test conditions. The evaluation results show that the final fMPE+MPE model achieves significant improvements compared to the baseline models. On the mono-language test sets, the best improvement is a relative error rate reduction of 28.4%. And on the code-mixing test set, it also achieves a relative error rate reduction of 8.1%. The within- and cross-language substitution error rate introduced in this paper also explicitly shows that fMPE/MPE training can effectively improve the model's within- and cross-language discriminabilities in our bilingual recognition tasks."}]