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- W2963317665 abstract "We introduce embeddings, which are based on unsupervised learning of context feature representations for spoken language. The embeddings were trained on up to 9500 hours of crawled English speech data without transcriptions or speaker information, by using a straightforward learning objective based on context and non-context discrimination with negative sampling. We use a Siamese convolutional neural network architecture to train Unspeech embeddings and evaluate them on speaker comparison, utterance clustering and as a context feature in TDNN-HMM acoustic models trained on TED-LIUM, comparing it to i-vector baselines. Particularly decoding out-of-domain speech data from the recently released Common Voice corpus shows consistent WER reductions. We release our source code and pre-trained Unspeech models under a permissive open source license." @default.
- W2963317665 created "2019-07-30" @default.
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- W2963317665 date "2018-09-02" @default.
- W2963317665 modified "2023-10-03" @default.
- W2963317665 title "Unspeech: Unsupervised Speech Context Embeddings" @default.
- W2963317665 doi "https://doi.org/10.21437/interspeech.2018-2194" @default.
- W2963317665 hasPublicationYear "2018" @default.
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