This is very interesting. I do something related: matching student oral reading transcripts back against the text they were assigned to read.Students will misread then re-read a sentence, skip sentences, skip paragraphs etc.
I think its similar because we both seek to assign audio time stamps to sentences and words.
But I wonder why your forced alignment algorithm is so heavy duty. (My head started to spin at CTC emissions. ) Probably yours is just way more thorough than mine,
My simplistic approach would have been to transcribe the audio. And then run a differencing script chapter by chapter matching the book text with the audio transcript. And then do something similar intra chapter to get sentence and word level time stamps.
I think its similar because we both seek to assign audio time stamps to sentences and words.
But I wonder why your forced alignment algorithm is so heavy duty. (My head started to spin at CTC emissions. ) Probably yours is just way more thorough than mine,
My simplistic approach would have been to transcribe the audio. And then run a differencing script chapter by chapter matching the book text with the audio transcript. And then do something similar intra chapter to get sentence and word level time stamps.