Are 10 000 hours of recordings necessary for every language

[JRMeyer]

>>> stergro
[August 12, 2019, 11:45am]

For some smaller languages the 10 000 hour aim in the common voice
project is very ambitious. How usable are smaller datasets for machine
learning? How important is the number of irregularities in a language in
this context?

Let's take an extreme example and look at the Esperanto dataset with 20
h. The language is completely regular, has no exceptions and the
pronounciation is always clear. How big must a dataset be to create
usefull results in this case? And apart from constructed languages: are
there differences between the natural languages or do they all need the
same amount of data?

Edit: 10 000 hours instead of 100 000 and mentioned the common voice
project.

[This is an archived TTS discussion thread from discourse.mozilla.org/t/are-10-000-hours-of-recordings-necessary-for-every-language]

[JRMeyer]

>>> lissyx
[August 12, 2019, 10:11am]

knows that
very well
![:slight_smile:](

[Archived Post]

[JRMeyer]

>>> reuben
[August 12, 2019, 10:35am]

Where did you get 100.000 hours from? We train our English release
models with slash ~3000 hours of data.

[Archived Post]

[JRMeyer]

>>> stergro
[August 12, 2019, 11:56am]

Interesting. And do you think these 3 000 hours are enough?

Sorry I was wrong, it says 10 000 hours. When you work on the common
voice website it always says something like: 'help us get a dataset of
10 000 hours for language X.' And it is also in the FAQs:

> Why is 10,000 validated hours the per language goal for capturing
> audio?

> This is approximately the number of hours required to train a
> production speech-to-text system.

edit: mentioned common voice

[Archived Post]

[JRMeyer]

>>> lissyx
[August 12, 2019, 11:29am]



> Sorry I was wrong, it says 10 000 hours. When you work on the website
> it always says something like: 'help us get a dataset of 10 000 hours
> for language X.' And it is also in the FAQs:

That's Common Voice, and yes, the value is mostly made from targetting
english. Each language has its own specifics. I don't know esperanto
enough to give a ballpark, francis might be able to.

[Archived Post]

[JRMeyer]

>>> ftyers
[August 12, 2019, 1:48pm]

There are many factors to take into account, among which:

accents (as there are nearly no L1 speakers, you get transfer from
the L1)
have recordings of all of them, your system is going to work better
than if you have 10 million speakers and only recordings of 100.
environments where you want to use the ASR system ... the noisier
the more data)

There is no research to my knowledge on comparing these factors to be
able to tell for a new language exactly how much data is necessary.

The best thing to do is 'try it and see', and report your results. You
can also try transfer learning, using the transfer-learning2 branch in
DeepSpeech or using a hybrid system like Kaldi, which can give better
results in low-data scenarios.

Hope this helps!

[Archived Post]

[JRMeyer]

>>> stergro
[August 12, 2019, 11:29pm]

Tanks for the answer, this sounds promissing, I would like to experiment
with this. Do you have a good starting point to learn about deep speech
apart from the github repository? Is this something I can achiev on a
consumer laptop with an ordinary linux on it or do I need more
computation power?

[Archived Post]

[JRMeyer]

>>> SamahZaro
[August 12, 2019, 3:02pm]



> - Diversity of speakers -- Esperanto speakers have quite diverse
> accents (as there are nearly no L1 speakers, you get transfer from
> the L1)
> - Number of speakers -- If you only have 10 speakers of a language
> and have recordings of all of them, your system is going to work
> better than if you have 10 million speakers and only recordings
> of 100.

, could you kindly say this in
other words. Did you mean the less the speakers the more the accuracy?

[Archived Post]

[JRMeyer]

>>> ftyers
[August 12, 2019, 5:51pm]

For accuracy on those speakers yes. For general accuracy (on any random
speaker) then the more speakers the better, obviously. But if you only
have 10 speakers of a language and you have recordings of all 10 of them
then obviously the results are going to be better.

[Archived Post]

[JRMeyer]

>>> SamahZaro
[August 13, 2019, 4:59pm]

slash
If I need to train a model to be speaker-independent and
age-independent, what is your suggestions to take into account?

I have a large dataset, with thousands of different speakers. However,
there are speakers which have hundreds/thousands of recordings, while
many others have only 1 recording. How should I deal with this?

Regarding age, I have much more adults' recordings in comparison with
child's. Should I filter out some adults' data for example?

[Archived Post]

[JRMeyer]

>>> stergro
[August 16, 2019, 7:51am]

The core of my question is: is the main part of a lingual neural network
about handeling all the exceptions and irregularities of a language or
is it more about handeling all the different voices and accents? Which
one is the harder problem that requires more data? I can guess that
since no one has ever tried machine learning with a completely regular
constructed language there is no answer to this question yet. But I
would guess that regularity could simplify things drastically.

mentioned:

> Diversity of speakers -- Esperanto speakers have quite diverse accents
> (as there are nearly no L1 speakers, you get transfer from the L1)

I would say there are quite a few L1 speakers, around one thousand
persons. But what makes Esperanto unusual is that in my experence native
Esperanto speakers sometimes tend to have stronger national accents than
someone who has learned Esperanto as a second language. I guess this is
because there is no Esperanto school and they only use Esperanto in
their family most of the year and only see the esperanto community on
congresses. This leads to a situation where the nation of origin is more
important in the question of accents than the question weather someone
is a native speaker or L2. I know complete beginners of Esperanto who
pronunce every word perfectly and could contribute to this dataset much
better than some native speakers.

> Number of speakers -- If you only have 10 speakers of a language and
> have recordings of all of them, your system is going to work better
> than if you have 10 million speakers and only recordings of 100.

The commitment to contribute to projects is much bigger in the esperanto
community than in other groups, one can clearly see this on things like
the esperanto wikipedia. Languages with comparable numbers of speakers
have much smaller numbers of articles. I guess if we advertise Common
Voice more in Esperanto magazines, congresses and websites we could get
a pretty good coverage, 1 000 of the 2 million L2 speakers seem possible
to me. Right now there are contributions from over 140 people in
Esperanto wich is already quite impressive for a constructed language.

[Archived Post]