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Word embeddings have become ubiquitous in NLP, especially when using neural networks. One of the assumptions of such representations is that words with similar properties have similar representation, allowing for better generalization from subsequent models. In the standard setting, two kinds of training corpora are used: a very large unlabeled corpus for learning the word embedding representations; and an in-domain training corpus with gold labels for training classifiers on the target NLP task. Because of the amount of data required to learn embeddings, they are trained on large corpus of written text. This can be an issue when dealing with non-canonical language, such as spontaneous speech: embeddings have to be adapted to fit the particularities of spoken transcriptions. However the adaptation corpus available for a given speech application can be limited, resulting in a high number of words from the embedding space not occurring in the adaptation space. We present in this paper a method for adapting an embedding space trained on written text to a spoken corpus of limited size. In particular we deal with words from the embedding space not occurring in the adaptation data. We report experiments done on a Part-Of-Speech task on spontaneous speech transcriptions collected in a call-centre. We show that our word embedding adaptation approach outperforms state-of-the-art Conditional Random Field approach when little in-domain adaptation data is available.