Toward the pre-cocktail party problem with TasTas++

Deep neural network with dual-path bi-directional long short-term memory (BiLSTM) block has been proved to be very effective in sequence modeling, especially in speech separation, e.g. DPRNN-TasNet \cite{luo2019dual}, TasTas \cite{shi2020speech}. In this paper, we propose two improvements of TasTas \cite{shi2020speech} for end-to-end approach to monaural speech separation in pre-cocktail party problems, which consists of 1) generate new training data through the original training batch in real time, and 2) train each module in TasTas separately. The new approach is called TasTas$+$, which takes the mixed utterance of five speakers and map it to five separated utterances, where each utterance contains only one speaker's voice. For the objective, we train the network by directly optimizing the utterance level scale-invariant signal-to-distortion ratio (SI-SDR) in a permutation invariant training (PIT) style. Our experiments on the public WSJ0-5mix data corpus results in 11.14dB SDR improvement, which shows our proposed networks can lead to performance improvement on the speaker separation task. We have open-sourced our re-implementation of the DPRNN-TasNet in https://github.com/ShiZiqiang/dual-path-RNNs-DPRNNs-based-speech-separation, and our TasTas$+$ is realized based on this implementation of DPRNN-TasNet, it is believed that the results in this paper can be reproduced with ease.Comment: arXiv admin note: substantial text overlap with arXiv:2001.08998, arXiv:1902.04891, arXiv:1902.00651, arXiv:2008.0314

Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks

In this paper we propose the utterance-level Permutation Invariant Training (uPIT) technique. uPIT is a practically applicable, end-to-end, deep learning based solution for speaker independent multi-talker speech separation. Specifically, uPIT extends the recently proposed Permutation Invariant Training (PIT) technique with an utterance-level cost function, hence eliminating the need for solving an additional permutation problem during inference, which is otherwise required by frame-level PIT. We achieve this using Recurrent Neural Networks (RNNs) that, during training, minimize the utterance-level separation error, hence forcing separated frames belonging to the same speaker to be aligned to the same output stream. In practice, this allows RNNs, trained with uPIT, to separate multi-talker mixed speech without any prior knowledge of signal duration, number of speakers, speaker identity or gender. We evaluated uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks and found that uPIT outperforms techniques based on Non-negative Matrix Factorization (NMF) and Computational Auditory Scene Analysis (CASA), and compares favorably with Deep Clustering (DPCL) and the Deep Attractor Network (DANet). Furthermore, we found that models trained with uPIT generalize well to unseen speakers and languages. Finally, we found that a single model, trained with uPIT, can handle both two-speaker, and three-speaker speech mixtures

Listening and grouping: an online autoregressive approach for monaural speech separation

This paper proposes an autoregressive approach to harness the power of deep learning for multi-speaker monaural speech separation. It exploits a causal temporal context in both mixture and past estimated separated signals and performs online separation that is compatible with real-time applications. The approach adopts a learned listening and grouping architecture motivated by computational auditory scene analysis, with a grouping stage that effectively addresses the label permutation problem at both frame and segment levels. Experimental results on the benchmark WSJ0-2mix dataset show that the new approach can outperform the majority of state-of-the-art methods in both closed-set and open-set conditions in terms of signal-to-distortion ratio (SDR) improvement and perceptual evaluation of speech quality (PESQ), even approaches that exploit whole-utterance statistics for separation, with relatively fewer model parameters