The robustness of this task can be improved by applying transforms like SPLICE and CMVN. An architecture of composed weighted finite-state transducers is used for this task by the open-source package Kaldi, which does not support the frequently-used CTC loss function in alignment during this task. Classical algorithms for this task extract 39 MFCC features for each time frame of a sliding window, then feed them into an (*) GMM-HMM. Context-dependent models are used in this task to account for allophones. (-5[1])Probabilistic methods for this task compute the product of an acoustic model and a language model to select the most likely word sequence given a sound signal. (10[1])For 10 points, speech synthesis (10[1])is the reverse of what natural language processing task used by digital assistants like Siri to process user input? (10[1])■END■