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Speech Recognition Is Not Solved

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Re: Speech Recognition Is Not Solved

#121

Post by okreallywtf »

aaronblohowiak wrote: post #117
fillskills wrote: post #43

Earlier quoted context omitted.

Wow, I wish there was an easy way to pay back star teachers or mentors.

Write them a handwritten letter and make sure it gets to them.

This. I did this with a math teacher (pre-calc and trig) I had at a community college years later when I was taking calculus etc in college and killing it (after having a less than stellar math education before and during highschool) and I think it was a huge deal to him. He really put his all into teaching that class and a lot of people probably went on to 4 yr college and didn't think much of it but after having professors that hardly spoke a word to their students and spent a straight hour just writing on a chalkboard I totally appreciated an invested, engaged math teacher.

Re: Speech Recognition Is Not Solved

#122

Post by dharma1 »

adrianbg wrote: post #103
canjobear wrote: post #10

It's always been a puzzle to me that published WER is so low, and yet when I use dictation (which is a lot--I use it for almost all text messages) I always have to make numerous corrections. This sentence is crucial and suggests a way to understand how WER can be apparently lower than human levels, yet ASR is still obviously imperfect: > When comparing models to humans, it’s important to check the nature of the mista…

The low WER numbers you've probably seen are for conversational telephone speech with constrained subjects. ASR is much harder when the audio source is farther away from the microphone and when the topic isn't constrained.

Very true. Which is why all the home assistants (Google Home, Amazon Echo etc) use array mics and beamforming - they get a much cleaner speech signal from far field audio, and better WER as a result

Re: Speech Recognition Is Not Solved

#123

Post by makecheck »

While understanding “any” speech is a great long-term goal, I wish they’d allow these systems to be a little dumber when I want them to.

One example: I often give the same commands to Alexa every day, even at similar times. Yet every few days it just utterly misunderstands those commands, picking words I have never even used before. It doesn’t even offer to choose similar commands I’ve used recently. Worse, the misunderstood command might trigger a paragraph of senseless babble about the misunderstood command, forcing me to shout over Alexa to make it hear the command I really wanted.

So please, please, add some “dumb” options. I want to pick some words, have the system learn them, and just obey commands 90% of the time.

Re: Speech Recognition Is Not Solved

#124

Post by adrianbg »

dharma1 wrote: post #122
adrianbg wrote: post #103

Earlier quoted context omitted.

The low WER numbers you've probably seen are for conversational telephone speech with constrained subjects. ASR is much harder when the audio source is farther away from the microphone and when the topic isn't constrained.

Very true. Which is why all the home assistants (Google Home, Amazon Echo etc) use array mics and beamforming - they get a much cleaner speech signal from far field audio, and better WER as a result

Exactly. This is also why Google sponsors the CHiME challenge, the existence of which is more proof that ASR is pretty far from solved.

http://spandh.dcs.shef.ac.uk/chime_challenge/

Re: Speech Recognition Is Not Solved

#125

Post by dragontamer »

gok wrote: post #73
dragontamer wrote: post #32

Earlier quoted context omitted.

Google has basically invented a special processor with a very, very, VERY weird architecture for these sorts of tasks: https://drive.google.com/file/d/0Bx4hafXDDq2EMzRNcy1vSUxtcEk... I don't think this level of computational power can be achieved on a modern CPU, or even a GPU! But GPUs are probably the closest analog to Google's absurdly parallel architecture. To get a GPU working at maximum performance, you either…

The TPU architecture isn't that weird...it's basically a hardware implementation of matrix multiplication. It also isn't a silver bullet for ASR, where neural networks are usually only used for a part of the recognition process.

> The TPU architecture isn't that weird

Its "weird" in the ways that matter: there's no commodity hardware in existence that replicates what a TPU does. The only place to get TPUs is through Google's cloud services.

CPUs are basically Von Neumann Architecture. GPUs (NVidia and AMD) are basically SIMD / SIMT systems.

Google's TPU is just something dramatically different, optimized yes for Matrix Multiplication, but its not something you can buy and use offline.

Re: Speech Recognition Is Not Solved

#126

Post by adrianbg »

dharma1 wrote: post #107
LandoCalrissian wrote: post #79

Earlier quoted context omitted.

I recently have been trying out open source solutions for voice recognition for a personal project and you are very correct that it lags very far behind proprietary solutions. Pocketsphinx is still very limited and Kaldi takes quite a bit to setup in a usable fashion. There were a few other options I looked at that I can't think of from the top of my head but were all in similar condition. As the article says latency…

Kaldi is the best, there was just Tensorflow integration added which will hopefully speed up development (though I haven't seen any pretrained models for that yet). Here's a blog post - http://www.googblogs.com/kaldi-now-offers-tensorflow-integra... The easiest way to deploy Kaldi is this - https://github.com/alumae/kaldi-gstreamer-server (or a docker image of the that)

Unfortunately that Tensorflow integration didn't include acoustic modeling, so you still need to use Kaldi's neural net toolkit for that.

Re: Speech Recognition Is Not Solved

#127

Post by oulipo »

johnramsden wrote: post #6

What I would like to see you would be an open source speech recognition system that is easy to use, and works. There is really no match for the proprietary solutions in the open source world right now, and that is disappointing. Not to say that there aren't open source speech recognition systems, they just aren't completely usable in the way proprietary solutions are. A lot of research goes into open source speech re…

I'm the cofounder of Snips.ai and we are building a 100% on-device Voice AI platform, which we want to open-source over time

You can build your voice assistants and run them for free on a Raspberry Pi 3, or Android

Re: Speech Recognition Is Not Solved

#128

Post by jjwiseman »

adrianbg wrote: post #120
dsacco wrote: post #16

Earlier quoted context omitted.

Is it an issue of open source software being inadequate, or is it the lack of sufficient training data and compute power local to your home? I’m under the impression that Google is mostly dogfooding its open source tooling for machine learning in GCP, and actually differentiates based on trained models and compute power.

The problem wrt open source / free solutions is data. Kaldi is open source and gets state of the art results -- but the data costs a lot of money. Training the models is doable on a commodity GPU although it takes quite a while.

I didn't realize Kaldi could get state of the art results. Do you say that because you know of people doing that, or is your comment based on knowing the architecture of Kaldi?

Re: Speech Recognition Is Not Solved

#129

Post by Cacti »

dragontamer wrote: post #32
ddevault wrote: post #8

Earlier quoted context omitted.

I agree - I don't care how good Google gets it, this is an unsolved problem until I can do it with open source tools operating without an internet connection.

Google has basically invented a special processor with a very, very, VERY weird architecture for these sorts of tasks: https://drive.google.com/file/d/0Bx4hafXDDq2EMzRNcy1vSUxtcEk... I don't think this level of computational power can be achieved on a modern CPU, or even a GPU! But GPUs are probably the closest analog to Google's absurdly parallel architecture. To get a GPU working at maximum performance, you either…

The level of the computation can be achieved just fine with a GPU or some co-processors. What the TPU excels at is performing forward inference very efficiently. So, you can't train on it, or perform arbitrary computation that well, but if you had a pre-trained neural net, you could run it very fast and with little power.

Re: Speech Recognition Is Not Solved

#130

Post by dharma1 »

adrianbg wrote: post #124
dharma1 wrote: post #122

Earlier quoted context omitted.

Very true. Which is why all the home assistants (Google Home, Amazon Echo etc) use array mics and beamforming - they get a much cleaner speech signal from far field audio, and better WER as a result

Exactly. This is also why Google sponsors the CHiME challenge, the existence of which is more proof that ASR is pretty far from solved. http://spandh.dcs.shef.ac.uk/chime_challenge/

Good stuff. Was looking into cheap array mics with linux drivers a few times in the past but not much is available.

Speech separation - Mitsubishi Research has done some pretty impressive stuff on that - http://www.merl.com/demos/deep-clustering. Haven't seen equivalents of that in open source ASR

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