13. The system of claim 12 , wherein processing the run-time sequence of the phonetic-context descriptors with the trained neural network in the corresponding sequence of neural network time steps to generate the one or more run-time predicted feature vectors comprises: receiving respective labels and phonetic context data in the phonetic-context descriptors of the run-time sequence; and generating the run-time predicted feature vectors based at least in part on the received respective labels and phonetic context data in the phonetic-context descriptors of the run-time sequence.
12. The system of claim 11 , wherein the training-time text strings and the predefined waveforms are part of a corpus of speech data, wherein the phonetic transcriptions of the training-time text strings are constructed from one or more labels each identifying a respective phonetic speech unit of the plurality of phonetic speech units, wherein each phonetic-context descriptor of the one or more training-time sequences includes one of the one or more labels, data indicating phonetic context of the identified respective phonetic speech unit, and data indicating time duration of the identified respective phonetic speech unit, wherein all of the phonetic-context descriptors of the one or more training-time sequences collectively comprise at least a portion of all possible combinations of phonetic contexts of each of the phonetic speech units of the plurality, and wherein training the neural network to map the one or more training-time sequences of phonetic-context descriptors into the training-time predicted feature vectors comprises: training the neural network to process at least the portion of all possible combinations of phonetic contexts of each of the phonetic speech units of the plurality; and training the neural network to generate the training-time predicted feature vectors based at least in part on processing the phonetic context data in the phonetic-context descriptors of the one or more training-time sequences.