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Word embedding or Word2Vec has been successful in offering semantics for textwords learned from the context of words. Audio Word2Vec was shown to offerphonetic structures for spoken words (signal segments for words) learned fromsignals within spoken words. This paper proposes a two-stage framework toperform phonetic-and-semantic embedding on spoken words considering the contextof the spoken words. Stage 1 performs phonetic embedding with speakercharacteristics disentangled. Stage 2 then performs semantic embedding inaddition. We further propose to evaluate the phonetic-and-semantic nature ofthe audio embeddings obtained in Stage 2 by parallelizing with text embeddings.In general, phonetic structure and semantics inevitably disturb each other. Forexample the words "brother" and "sister" are close in semantics but verydifferent in phonetic structure, while the words "brother" and "bother" are inthe other way around. But phonetic-and-semantic embedding is attractive, asshown in the initial experiments on spoken document retrieval. Not only spokendocuments including the spoken query can be retrieved based on the phoneticstructures, but spoken documents semantically related to the query but notincluding the query can also be retrieved based on the semantics.

Phonetic-and-Semantic Embedding of Spoken Words with Applications in Spoken Content Retrieval