Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a two-pass framework of mispronunciation detection and diagnosis (MD&D) — detection follod by diagnosis, without the need of explicit error pattern modeling, so that the main efforts can be devoted to improving acoustic modeling by discriminative training (or by applying alternative models like neural nets). The framework instantiates a set of anti-phones and a filler model in addition to the original phone model set, and crafts a general and compact phone error detection network. The detection network guarantees full coverage of all possible error patterns while maximally exploits the constraint offered by the text prompt. Specifically, it includes anti-phones to detect substitutions, filler model to detect insertions, and skips to detect deletions, so there is no prior assumptions on the possible form of error patterns. The subsequent diagnosis step expands the detected insertions and substitutions into phone networks, after which another recognition pass reveals the true identities of the detected errors. The crux of the trick is to bring down the modeling and recognition granularity down in the detection pass. Discriminative training (DT) of the detection and diagnosis models by minimizing the two expected full-sequence phone-level errors in the respective passes brings down the overall phone-level MD&D error by a relative of 40%. In particular, visualization of models in the framework shows that discriminative training effectively separates the canonical phones and their anti-phones.
['Xiaojun Qian', 'Helen M. Meng', 'Frank K. Soong']
I am writing a research paper on a idea A two-pass framework of mispronunciation detection & diagnosis for computer-aided pronunciation training
[{"from": "human", "value": "I am writing a research paper on a idea A two-pass framework of mispronunciation detection & diagnosis for computer-aided pronunciation training"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a two-pass framework of mispronunciation detection and diagnosis (MD&D) \u2014 detection follod by diagnosis, without the need of explicit error pattern modeling, so that the main efforts can be devoted to improving acoustic modeling by discriminative training (or by applying alternative models like neural nets). The framework instantiates a set of anti-phones and a filler model in addition to the original phone model set, and crafts a general and compact phone error detection network. The detection network guarantees full coverage of all possible error patterns while maximally exploits the constraint offered by the text prompt. Specifically, it includes anti-phones to detect substitutions, filler model to detect insertions, and skips to detect deletions, so there is no prior assumptions on the possible form of error patterns. The subsequent diagnosis step expands the detected insertions and substitutions into phone networks, after which another recognition pass reveals the true identities of the detected errors. The crux of the trick is to bring down the modeling and recognition granularity down in the detection pass. Discriminative training (DT) of the detection and diagnosis models by minimizing the two expected full-sequence phone-level errors in the respective passes brings down the overall phone-level MD&D error by a relative of 40%. In particular, visualization of models in the framework shows that discriminative training effectively separates the canonical phones and their anti-phones."}]