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Aside from issues relating to background noise on the soundtrack, the subtitles are frequently abridged from the spoken word in the interests of space and / or

Aside from issues relating to background noise on the soundtrack, the subtitles are frequently abridged from the spoken word in the interests of space and / or readability, so you'd need to account for that in your algorithm. If it were me... Project Gutenberg has free books available in both audio and text formats. You may well again run into issues with the spoken and written text not exactly matching (it's not something I've looked into to know) but I wouldn't be surprised if it was rather less than what I've observed in subtitles, and the data concerned is in a more easily parsed format.

rcthompson 15y ago
Audio recordings of book readings are less practical than subtitles because they are not synchronized. Every subtitle in a film is associated with the sound clip that plays while it is visible, whereas for an audiobook or similar, any algorithm would have to "align" the audio and text in order to obtain usable training data, and then it would have to deal with the errors introduced by this process.
- eftpotrm 15y ago
  Hmm, good point, though still against a cleaner audio signal, easier input data to process and (likely) closer matching of text. I'm not remotely involved with the field so I won't indulge in further wild speculation, but an interesting balance.
sycren 15y ago
As 0x12 states further down, noise can be seen as beneficial. By having such a huge dataset, perhaps it would be possible to advance the technology of speech recognition to transcribe speech in busy places as needed for in mobile applications where the user is not in a quiet room.
- eftpotrm 15y ago
  Perhaps, but I wouldn't use that as a starting dataset; noise resilience and a training set for enhancing this functionality is surely better developed on top of a working implementation for a lower-noise input? Better to build the easier solution and reinforce it for hard problems than try to go straight at the hard problems. Put it another way; which would you start by teaching a student: the easy situations or the more complex situations?

rcthompson 15y ago

Audio recordings of book readings are less practical than subtitles because they are not synchronized. Every subtitle in a film is associated with the sound clip that plays while it is visible, whereas for an audiobook or similar, any algorithm would have to "align" the audio and text in order to obtain usable training data, and then it would have to deal with the errors introduced by this process.

eftpotrm 15y ago
Hmm, good point, though still against a cleaner audio signal, easier input data to process and (likely) closer matching of text. I'm not remotely involved with the field so I won't indulge in further wild speculation, but an interesting balance.

sycren 15y ago

As 0x12 states further down, noise can be seen as beneficial. By having such a huge dataset, perhaps it would be possible to advance the technology of speech recognition to transcribe speech in busy places as needed for in mobile applications where the user is not in a quiet room.

eftpotrm 15y ago
Perhaps, but I wouldn't use that as a starting dataset; noise resilience and a training set for enhancing this functionality is surely better developed on top of a working implementation for a lower-noise input? Better to build the easier solution and reinforce it for hard problems than try to go straight at the hard problems. Put it another way; which would you start by teaching a student: the easy situations or the more complex situations?