Recent work on transformer transducers with limited right (and left) context seem to give decent results as well: https://arxiv.org/abs/2002.02562
I opened the pdf, did ctrl+F for 'github', got zero results. Have you reproduced their "decent results"?
ESPnet apparently already has implementations of versions of transformer transducers with RNN-T loss (though, with different network architecture). At least the paper cites results on a freely available dataset, right?
Your point about needing a dataset made me think about how a post on hackernews like this may be a good way to get data. How many people would contribute by reading a prompt if they visited a link like this and had the option to donate some data? That would get many distinct voices and microphones and some different conditions. The article mentions that they used a dataset composed of 100 hours of audiobooks. A comme…
You might be interested in a project, doing exactly that: https://voice.mozilla.org/ Audio data of people reading prompts is quite common, what is missing for robust voice recognition is plenty of data of e.g. people screaming it across the room. There is only so much physics simulations can do.
Good article. Speech recognition for real time use cases must get a really working open source solution. I have been evaluating deepspeech, which is okay. but there is lots of work needed to make it working close to Google Speech engine. Apart from a good Deep neural network, a good speech recognition system needs two important things: 1. Tons of diverse data sets (real world) 2. Solution for Noise - Either de-noise…
Your point about needing a dataset made me think about how a post on hackernews like this may be a good way to get data. How many people would contribute by reading a prompt if they visited a link like this and had the option to donate some data? That would get many distinct voices and microphones and some different conditions. The article mentions that they used a dataset composed of 100 hours of audiobooks. A comme…
There might be some sampling bias with an HN user dataset. At my company, many of our customer service calls are from older people, especially women, who call because they don't like using the internet (or they don't even have internet). Different voices and patterns of speech. This could be a really different demographic from HN users.
I opened the pdf, did ctrl+F for 'github', got zero results. Have you reproduced their "decent results"?
ESPnet apparently already has implementations of versions of transformer transducers with RNN-T loss (though, with different network architecture). At least the paper cites results on a freely available dataset, right?