  Generating spoken word embeddings that possess semantic information is a
fascinating topic. Compared with text-based embeddings, they cover both
phonetic and semantic characteristics, which can provide richer information and
are potentially helpful for improving ASR and speech translation systems. In
this paper, we review and examine the authenticity of a seminal work in this
field: Speech2Vec. First, a homophone-based inspection method is proposed to
check the speech embeddings released by the author of Speech2Vec. There is no
indication that these embeddings are generated by the Speech2Vec model.
Moreover, through further analysis of the vocabulary composition, we suspect
that a text-based model fabricates these embeddings. Finally, we reproduce the
Speech2Vec model, referring to the official code and optimal settings in the
original paper. Experiments showed that this model failed to learn effective
semantic embeddings. In word similarity benchmarks, it gets a correlation score
of 0.08 in MEN and 0.15 in WS-353-SIM tests, which is over 0.5 lower than those
described in the original paper. Our data and code are available.
