Arxiv – DeepSpeech: Scaling up end-to-end speech recognition
Baidu researchers present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a “phoneme.” Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called DeepSpeech, outperforms previously published results on the widely studied Switchboard Hub5’00, achieving 16.5% error on the full test set. DeepSpeech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.
In restaurant settings and other loud places where other commercial speech recognition systems fail, the deep learning model proved accurate nearly 81 percent of the time. Commercial speech-recognition APIs against which Deep Speech was tested, including those for Microsoft Bing, Google and Wit.AI, topped out at nearly 65 percent accuracy in noisy environments. Those results probably underestimate the difference in accuracy, said Baidu Chief Scientist Andrew Ng, who worked on Deep Speech along with colleagues at the company’s artificial intelligence lab in Palo Alto. His team could only compare accuracy where the other systems all returned results rather than empty strings.
Ng said that while the research is still just research for now, Baidu is definitely considering integrating it into its speech-recognition software for smartphones and connected devices such as Baidu Eye. The company is also working on an Amazon Echo-like home appliance called CoolBox, and even a smart bike.
The software attempts to mimic, in very primitive form, the activity in layers of neurons in the neocortex, the 80 percent of the brain where thinking occurs, so deep learning systems learn to recognize patterns in digital representations of sounds, images, and other data–ideally lots and lots of data.
SOURCES – Youtube, Arxiv, Gigaom, Forbes
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