There are a number of papers tackling a similar task [0][1][2] for anyone who is interested. There isn't enough information to tell exactly what is going on wit

There are a number of papers tackling a similar task [0][1][2] for anyone who is interested. There isn't enough information to tell exactly what is going on with Deepgram, but one way to approach this would be to construct a shared embedding space for words/phrases and speech. These types of embedding spaces are powerful [3][4][5], but not magic. Cool demo, looking forward to seeing more detail about what is going on. However I would quibble with the STT WER quoted above. Maybe in noisy environments with unknown speakers (and no voice normalization) this is accurate, but the kinds of clean speech in the demo perform really well in modern recognition engines (on benchmark data, to be fair c.f. MSR 6.3% and IBM at ~6.9%). Most word searches over speech to text work over soft matches (or ideally beam search over most likely partial phoneme/word part matches), rather than hard matches so it seems like a bit of a straw man comparison in this case. [0] http://research.google.com/pubs/pub42543.html http://research.google.com/pubs/pub42543.html [1] https://arxiv.org/abs/1510.01032 https://arxiv.org/abs/1510.01032 [2] https://sigport.org/sites/default/files/gloveNNLM_kaudhkhasi_icassp2016.pdf https://sigport.org/sites/default/files/gloveNNLM_kaudhkhasi... [3] https://arxiv.org/abs/1502.03044 https://arxiv.org/abs/1502.03044 [4] http://www-personal.umich.edu/~reedscot/files/icml2016.pdf http://www-personal.umich.edu/~reedscot/files/icml2016.pdf [5] https://arxiv.org/abs/1411.2539 https://arxiv.org/abs/1411.2539