1. A method of training an expert-assisted phoneme recognition neural network system, the method comprising: at an expert-assisted phoneme recognition neural network system configured to generate one or more phoneme candidates as recognized within audible signal data, the expert-assisted phoneme recognition neural network system including an ensemble phoneme recognition neural network and a phoneme-specific experts system: selecting a target problematic phoneme; synthesizing a targeted training data set including an overemphasis of examples of the target problematic phoneme; synthesizing respective problematic phoneme-specific weight values for a problematic phoneme-specific expert neural network (PPENN) included in the phoneme-specific experts system, in accordance with a determination that the respective problematic phoneme-specific weight values satisfy an error convergence threshold, by: providing the synthesized target training data set to the PPENN; determining an output of the PPENN in response to providing the synthesized target training data set to the PPENN; updating the respective problematic phoneme-specific weight values for the PPENN based on a function of the output of the PPENN; and iteratively providing the synthesized target training data to the PPENN and updating the respective problematic phoneme-specific weight values until a set of updated weights satisfies the error convergence threshold.
1. A method of training an expert-assisted phoneme recognition neural network system, the method comprising: at an expert-assisted phoneme recognition neural network system configured to generate one or more phoneme candidates as recognized within audible signal data, the expert-assisted phoneme recognition neural network system including an ensemble phoneme recognition neural network and a phoneme-specific experts system: selecting a target problematic phoneme; synthesizing a targeted training data set including an overemphasis of examples of the target problematic phoneme; synthesizing respective problematic phoneme-specific weight values for a problematic phoneme-specific expert neural network (PPENN) included in the phoneme-specific experts system, in accordance with a determination that the respective problematic phoneme-specific weight values satisfy an error convergence threshold, by: providing the synthesized target training data set to the PPENN; determining an output of the PPENN in response to providing the synthesized target training data set to the PPENN; updating the respective problematic phoneme-specific weight values for the PPENN based on a function of the output of the PPENN; and iteratively providing the synthesized target training data to the PPENN and updating the respective problematic phoneme-specific weight values until a set of updated weights satisfies the error convergence threshold. 12. The method of claim 1 , further comprising synthesizing respective ensemble weight values for the ensemble phoneme recognition neural network by training the ensemble phoneme recognition neural network in combination with the problematic phoneme-specific expert neural network (PPENN) included in the phoneme-specific experts system.