Subject: Re: [AUDITORY] Why is it that joint speech-enhancement with ASR is not a popular research topic? From: Phil Green <p.green@xxxxxxxx> Date: Mon, 25 Jun 2018 09:07:45 +0100 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>and, for conventional HMM based systems, you get the best performance when the training data is a good match to the material to be recognised. So if enhancing the speech worsens the match performance will go down. On 25/06/2018 08:15, Laszlo Toth wrote: > On Sun, 24 Jun 2018, Samer Hijazi wrote: > >> It is easy to see that ASR would benefit from speech enhancement, and >> speech enhancement would benefit from ASR. But there is very limited >> research and publications in this direction vs the 100's of publications on >> stand alone ASR, why is that? > The currently dominant directon in ASR is "end-to-end learning". > That is, to drop any hand-crafted feature extraction step from the > processing chain, and let the deep learning algorithm solve the whole > problem "as is". While many people doubt that this is the good direction > (at least, with the current limited-capability learning algorithms), there > is a strong pressure to prefer these end-to-end models over a two-step > model (I mean enhancement+recognition). > > Laszlo Toth > Hungarian Academy of Sciences * > Research Group on Artificial Intelligence * "Failure only begins > e-mail: tothl@xxxxxxxx * when you stop trying" > http://www.inf.u-szeged.hu/~tothl * -- *** note email is now p.green@xxxxxxxx *** Professor Phil Green SPandH Dept of Computer Science University of Sheffield *** note email is now p.green@xxxxxxxx ***