  The paper copes with the task of automatic assessment of second language
proficiency from the language learners' spoken responses to test prompts. The
task has significant relevance to the field of computer assisted language
learning. The approach presented in the paper relies on two separate modules:
(1) an automatic speech recognition system that yields text transcripts of the
spoken interactions involved, and (2) a multiple classifier system based on
deep learners that ranks the transcripts into proficiency classes. Different
deep neural network architectures (both feed-forward and recurrent) are
specialized over diverse representations of the texts in terms of: a reference
grammar, the outcome of probabilistic language models, several word embeddings,
and two bag-of-word models. Combination of the individual classifiers is
realized either via a probabilistic pseudo-joint model, or via a neural mixture
of experts. Using the data of the third Spoken CALL Shared Task challenge, the
highest values to date were obtained in terms of three popular evaluation
metrics.
