The Silicon Valley AI Lab is Baidu's US-based research group, started a bit more than two years ago by Andrew Ng and Adam Coates. The mission of SVAIL is to build hard AI technologies that let us impact hundreds of millions of users.
We work on deep learning for speech and language; systems research to drive scalability of deep learning models; and new product development to bring research success to end users.
We are hiring for lots of roles in all three of these areas. The above link has the full list, but I'd like to draw particular attention to our need for software engineers (the "Software Engineer - AI Product" role). There is a huge opportunity to be an early member of a newly-formed team responsible for building the next generation of AI-enabled products. No prior experience in machine learning or AI necessary -- if you are a strong engineer, we feel confident we can teach the needed ML.
Apply at the link above, or email [email protected] if you have questions (or ask right here). Thanks!
Both Kaldi[1] and CMU Sphinx[2] are high-quality open source speech systems. I know for a fact that Kaldi includes support for DNN acoustic models (I'm less familiar with Sphinx).
Mostly this, though it's not so black-and-white. The paper discusses results from a DNN-HMM system (Maas et al., using Kaldi) trained on 2k hours, and it does provide a small generalization improvement over 300 hours.
Much of the excitement about deep learning -- which we see as well in DeepSpeech -- is that these models continue to improve as we provide more training data. It's not obvious a priori that results will keep getting better after thousands of hours of speech. We're exited to keep advancing that frontier.
For a single utterance, it's fast enough that we can produce results in real time. Of course, building a production system for millions of users might require just a bit more engineering work...
Hi Jerome, those are great results! We got an email this morning from someone else on the Watson team pointing out that we didn't include the latest IBM number -- we'll be sure to update the results in the next version of the paper (three cheers for arXiv).
Of course, we openly say in the paper that we don't have the best result on easy subset of Hub5'00 (we had it as 11.5%). We're more interested in advancing the state of the art on challenging, noisy, varied speech. Of course we'll be working to push the SWB number down too :)
As in many things, it's a combination of both. For example:
- We wanted no more than one recurrent layer, as it's a big bottleneck to parallelization.
- The recurrent layer should go "higher" in the network, as it's more effective at propagating long-range context when using the network's learned feature representation than using raw input values.
Other decisions are guided by a combination of trial+error and intuition. We started on much smaller datasets which can give you a feel for the bias/variance tradeoff as a function of the number of layers, the layer sizes, and other hyperparameters.
The Silicon Valley AI Lab is Baidu's US-based research group, started a bit more than two years ago by Andrew Ng and Adam Coates. The mission of SVAIL is to build hard AI technologies that let us impact hundreds of millions of users.
We work on deep learning for speech and language; systems research to drive scalability of deep learning models; and new product development to bring research success to end users.
We are hiring for lots of roles in all three of these areas. The above link has the full list, but I'd like to draw particular attention to our need for software engineers (the "Software Engineer - AI Product" role). There is a huge opportunity to be an early member of a newly-formed team responsible for building the next generation of AI-enabled products. No prior experience in machine learning or AI necessary -- if you are a strong engineer, we feel confident we can teach the needed ML.
Apply at the link above, or email [email protected] if you have questions (or ask right here). Thanks!