  Embedding acoustic information into fixed length representations is of
interest for a whole range of applications in speech and audio technology. Two
novel unsupervised approaches to generate acoustic embeddings by modelling of
acoustic context are proposed. The first approach is a contextual joint factor
synthesis encoder, where the encoder in an encoder/decoder framework is trained
to extract joint factors from surrounding audio frames to best generate the
target output. The second approach is a contextual joint factor analysis
encoder, where the encoder is trained to analyse joint factors from the source
signal that correlates best with the neighbouring audio. To evaluate the
effectiveness of our approaches compared to prior work, two tasks are conducted
-- phone classification and speaker recognition -- and test on different TIMIT
data sets. Experimental results show that one of the proposed approaches
outperforms phone classification baselines, yielding a classification accuracy
of 74.1%. When using additional out-of-domain data for training, an additional
3% improvements can be obtained, for both for phone classification and speaker
recognition tasks.
