Hi all,
As our group has worked on related topics, please allow me to mention some plugs, I mean, references.
- Using features derived from an ASR output (itself computed on the output of a first speech enhancement network) as input to a second enhancement system: Hakan Erdogan, John R. Hershey, Shinji Watanabe, Jonathan Le Roux, "Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks," in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2015), Apr. 2015.
http://www.jonathanleroux.org/pdf/Erdogan2015ICASSP04.pdf- Training jointly a speech separation network and an end-to-end ASR network for multi-speaker ASR: Shane Settle, Jonathan Le Roux, Takaaki Hori, Shinji Watanabe, John R. Hershey, "End-to-End Multi-Speaker Speech Recognition," in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Apr. 2018.
http://www.jonathanleroux.org/pdf/Settle2018ICASSP04.pdf
There has also been a lot of work in multi-channel settings, around the CHiME3 and 4 workshops. In particular, training beamforming and ASR networks jointly has recently been proposed:
-
Tsubasa Ochiai, Shinji Watanabe, Takaaki Hori, John R. Hershey, "Multichannel End-to-end Speech Recognition", International Conference on Machine Learning (ICML), August 2017.
- Tsubasa Ochiai, Shinji Watanabe, Shigeru Katagiri, "Does speech enhancement work with end-to-end ASR objectives?: Experimental analysis of multichannel end-to-end ASR", IEEE International Workshop on Machine Learning for Signal Processing (MLSP), October 2017.
Best,
Jonathan
Jonathan Le Roux <Jonathan.Le-Roux@xxxxxxxxxxxxxx>
Senior Principal Research Scientist, Speech & Audio Team Leader
MERL - Mitsubishi Electric Research Laboratories
201 Broadway, 8th Floor, Cambridge, MA 02139
Tel.: +1-617-621-7547 Fax: +1-617-621-7550
As an early proponent of a combined approach to speech understanding and separation, I agree with most that has been said here. However, I would like to add that we could get easier to our ultimate goal if the dynamics, the changes, were dealt with in a more explicit fashion. So, unashamedly, I would like to recommend all of you to take a look at our book “Speech: A dynamic process” (Carré, Divenyi, and Mrayati; de Gruyter 2017).
Pierre Divenyi
Sent from my autocorrecting iPad
Hi Phil,
Thanks for your insightful response and pointing me to your duplication on this topic from 2003. I am particularly intrigued by your comment,
I am particularly intrigued with your comment:
"
It would be wrong to start with clean speech, add noise, use that as input and clean speech + text as training targets, because in real life speech& other sound sources don't combine like that.
"
There are many recent publication on speech enhancement
that are using a simple additive noise model, and sometimes RIR simulator, and they are publishing impressive results. Is there a need to incorporate any thing beyond RIR to generalize the training dataset to create a solution that would work properly in the real world?
Regards,
Samer
On 25/06/2018 17:00, Samer Hijazi
wrote:
Thanks Laszlo and Phil,
I am not speaking about doing ASR in two steps, i am
speaking about doing the ASR and speech enhancement jointly in
multi-objective learning process.
Are, you mean multitask learning. That didn't come over at all in
your first mail.
There are many papers showing if you used related objective
resumes to train your network, you will get better results on
both objectives than what you would get if you train for each
one separately.
An early paper on this, probably the first application to ASR, was
Parveen & Green, Multitask Learning in Connectionist
Robust ASR using Recurrent Neural Networks, Eurospeech 2003.
And it seams obvious that if we used speech contents (i.e.
text) and perfect speech waveform as two independent but
correlated targets, we will end up with a better text
recognition and better speech enhancement; am i missing
something?
It would be wrong to start with clean speech, add noise, use that as
input and clean speech + text as training targets, because in real
life speech & other sound sources don't combine like that.
That's why the spectacular results in the Parveen/Green paper are
misleading..
HTH
--
*** note email is now p.green@xxxxxxxxxx ***
Professor Phil Green
SPandH
Dept of Computer Science
University of Sheffield
*** note email is now p.green@xxxxxxxxxx ***