How computers learn to turn sounds into words with recurrent neural networks
Many tasks we use every day, like turning speech into text or translating sentences, are really about changing one stream of things into another — called sequence transduction. A kind of model known as recurrent neural networks looks at data over time and learns patterns even when the signal stretchs or shifts a bit. Traditionally these models needed a pre-made match between input and output, and finding that match was the hardest part. Newer systems do the whole job from start to finish as an end-to-end process that figures out the alignment and how long the result should be, so fewer steps and less guess work is needed. Early tests on speech sounds show this idea can improve how machines hea…
How computers learn to turn sounds into words with recurrent neural networks
Many tasks we use every day, like turning speech into text or translating sentences, are really about changing one stream of things into another — called sequence transduction. A kind of model known as recurrent neural networks looks at data over time and learns patterns even when the signal stretchs or shifts a bit. Traditionally these models needed a pre-made match between input and output, and finding that match was the hardest part. Newer systems do the whole job from start to finish as an end-to-end process that figures out the alignment and how long the result should be, so fewer steps and less guess work is needed. Early tests on speech sounds show this idea can improve how machines hear and write, making tools for speech recognition simpler and faster. It feel like a small step but one that could make voice tech easier for everyone to use.
Read article comprehensive review in Paperium.net: Sequence Transduction with Recurrent Neural Networks
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