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SINGLE CHANNEL SPEECH SEPARATION WITH CONSTRAINED UTTERANCE LEVEL PERMUTATION INVARIANT TRAINING USING GRID LSTM

Citation Author(s):
CHENGLIN XU, WEI RAO, XIONG XIAO, ENG SIONG CHNG, HAIZHOU LI
Submitted by:
Chenglin Xu
Last updated:
20 April 2018 - 12:38am
Document Type:
Presentation Slides
Document Year:
2018
Event:
Presenters Name:
CHENGLIN XU
Paper Code:
AASP-L1.2

Abstract 

Abstract: 

Utterance level permutation invariant training (uPIT) tech- nique is a state-of-the-art deep learning architecture for speaker independent multi-talker separation. uPIT solves the label ambiguity problem by minimizing the mean square error (MSE) over all permutations between outputs and tar- gets. However, uPIT may be sub-optimal at segmental level because the optimization is not calculated over the individual frames. In this paper, we propose a constrained uPIT (cu- PIT) to solve this problem by computing a weighted MSE loss using dynamic information (i.e., delta and acceleration). The weighted loss ensures the temporal continuity of output frames with the same speaker. Inspired by the heuristics (i.e., vocal tract continuity) in computational auditory scene analy- sis, we then extend the model by adding a Grid LSTM layer, that we name it as cuPIT-Grid LSTM, to automatically learn both temporal and spectral patterns over the input magnitude spectrum simultaneously. The experimental results show 9.6% and 8.5% relative improvements on WSJ0-2mix dataset under both closed and open conditions comparing with the uPIT baseline.

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Dataset Files

ICASSP2018_1844.pdf

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