Speaker Diarization with LSTM: Android Demo
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- Опубліковано 16 бер 2019
- Home page: google.github.io/speaker-id/p...
Paper: arxiv.org/abs/1710.10468
Poster: 162.242.252.85/documents/speaker-diarization-lstm
Tutorial: • [ICASSP 2018] Google's...
The audios were being played from a speaker, so there were some acoustic distortions.
I was holding another phone to record the videos with single hand, so the videos are not very stable.
Udemy online course on speaker recognition: www.udemy.com/course/speaker-...
Udemy online course on speaker diarization: www.udemy.com/course/diarizat... - Наука та технологія
![[Interspeech 2019] VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking](http://i.ytimg.com/vi/gnRX2lzepz0/mqdefault.jpg)
![[Interspeech 2019] VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking](/img/tr.png)
![[ICASSP 2018] Google's Diarization System: Speaker Diarization with LSTM](http://i.ytimg.com/vi/pjxGPZQeeO4/mqdefault.jpg)
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Absolutely amazing work!
Wow!
The best example I've ever seen so far!
Can you share this example via code?
Have you seen anything like this done on iOS using the same principle?
Is this APP demo available for the public? Would love to give it a try
Is this offline or online system?
do you need to enroll the speakers' voice first? or it can distinguish the speakers without enrollment process?
Enrollment is not needed for diarization.
@@QuanWang Thanks for the response. So with diarization, you can only know "when" the speaker change, but can not know who is speaking? or we can also enhance the function to know "who" and "when"?
@@user-yy3nm5hu4g You know when and who. But this "who" is anonymized. It's like speaker A and speaker B, not Patrick and Mary.
@@user-yy3nm5hu4g Please check the tutorial video.
@@QuanWang Will do! Thanks a lot =)
any one please send the project