PofoliaShared via Pofolia

IEEE/ACM Transactions on Audio Speech and Language Processing· 2017Q1

Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks

Morten Kolbæk, Dong Yu, Zheng‐Hua Tan, Jesper Jensen

Short summary

Utterance-level permutation invariant training (uPIT) enables deep recurrent neural networks to separate multitalker speech without prior knowledge of signal duration, number of speakers, or speaker identity.

AI-generated from the title and abstract; the full text is not read.

TakeawaysIn the app
Key pointsIn the app
Ask the paperIn the app

The rest is in the Pofolia app

Takeaways, key points and questions to the paper; new summaries every day for your field. Free.

Sign in on the web to open

Field: Signal Processing

Signal ProcessingComputer Science