Over the past several years, deep learning has shown remarkable success on some of the world’s most difficult computer science challenges, from image classification and captioning to translation to model visualization techniques. Recently we announced improvements to Google Voice transcription using Long Short-term Memory Recurrent Neural Networks (LSTM RNNs)—yet another place neural networks are improving useful services. We thought we’d give a little more detail on how we did this.

Since it launched in 2009, Google Voice transcription had used Gaussian Mixture Model (GMM) acoustic models, the state of the art in speech recognition for 30+ years. Sophisticated techniques like adapting the models to the speaker’s voice augmented this relatively simple modeling method.

Then around 2012, Deep Neural Networks (DNNs) revolutionized the field of speech recognition. These multi-layer networks distinguish sounds better than GMMs by using “discriminative training,” differentiating phonetic units instead of modeling each one independently.

But things really improved rapidly with Recurrent Neural Networks (RNNs), and especially LSTM RNNs, first launched in Android’s speech recognizer in May 2012. Compared to DNNs, LSTM RNNs have additional recurrent connections and memory cells that allow them to “remember” the data they’ve seen so far—much as you interpret the words you hear based on previous words in a sentence.

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