We study four data augmentation (DA) techniques and two model architectures on realistic data for sound event localization and detection (SELD). First, based on ResNet-Conformer (RC), we compare the four DA approaches on the realistic DCASE 2022 SELD test set which is often not easy to handle due to room reverberations and audio overlaps in spontaneous recordings. Experimental results show that, except for audio channel swapping (ACS), the other three data augmentation methods that work well on the simulated SELD data set are no longer effective due to mismatches between simulated and realistic conditions. Next, using ACS-based augmentation, the two improved ResNet-Conformer networks further enhance SELD performances in realistic conditions. By incorporating these two sets of techniques, our overall system ranked the first place in SELD task of the DCASE 2022 Challenge.

This study focuses on the design of a loss function for a deep neural network (DNN)-based model with two branches, which is used to solve sound event localization and detection (SELD) on low-resource realistic data. To this end, we employ a secondary network for audio classification, which provides global event information to the main network, enabling it to make robust SELD predictions. Furthermore, we suggest utilizing a momentum strategy for direction-of-arrival (DOA) estimation, taking advantage of the strong temporal consistency of sound events, thereby effectively reducing localization error. Lastly, we incorporate a regularization term into the loss function to alleviate the overfitting problem on the small dataset. We evaluate our proposed methods on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Task 3 dataset, and the results demonstrate consistent improvements in SELD performance. In comparison to the baseline system, the proposed loss function yields significantly improved results for both localization and detection metrics on realistic data. Moreover, the proposed loss function demonstrates its ability to generalize across different network architectures, as evidenced by the consistent improvements achieved.

To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed. Our two-stage system leverages on an ad-hoc score combination based on two CNN classifiers: (i) the first CNN classifies acoustic inputs into one of three broad classes, and (ii) the second CNN classifies the same inputs into one of ten finergrained classes. Three different CNN architectures are explored to implement the two-stage classifiers, and a frequency sub-sampling scheme is investigated. Moreover, novel data augmentation schemes for ASC are also investigated. Evaluated on DCASE 2020 Task 1a, our results show that the proposed ASC system attains a state-of-the-art accuracy on the development set, where our best system, a two-stage fusion of CNN ensembles, delivers a 81.9% average accuracy among multi-device test data, and it obtains a significant improvement on unseen devices. Finally, neural saliency analysis with class activation mapping (CAM) gives new insights on the patterns learnt by our models.

This paper presents our main contributions of acoustic modeling for multi-array multi-talker speech recognition in the CHiME-6 Challenge, exploring different strategies for acoustic data augmentation and neural network architectures. First, enhanced data from our front-end network preprocessing and spectral augmentation are investigated to be effective for improving speech recognition performance. Second, several neural network architectures are explored by different combinations of deep residual network (ResNet), factorized time delay neural network (TDNNF) and residual bidirectional long short-term memory (RBiLSTM). Finally, multiple acoustic models can be combined via minimum Bayes risk fusion. Compared with the official baseline acoustic model, the proposed solution can achieve a relatively word error rate reduction of 19% for the best single ASR system on the evaluation data, which is also one of main contributions to our top system for the Track 1 tasks of the CHiME-6 Challenge.

A new cross-entropy-guided measure (CEGM) is proposed to indirectly assess accuracies of automatic speech recognition (ASR) of degraded speech with a speech enhancement front-end and without directly performing ASR experiments. The proposed CEGM is calculated in three steps, namely: (1) a low-level representations via feature extraction, (2) a high-level nonlinear mapping using an acoustic model, and (3) a final CEGM calculation between the high-level representations of clean and enhanced speech. Specifically, state posterior probabilities from outputs of conventional hybrid acoustic model of the target ASR system are adopted as the high-level representations and a cross-entropy criterion is used to calculate the CEGM. Due to CEGM's differentiability, it can also be used to replace the conventional minimum mean squared error (MMSE) criterion as an objective function for deep neural network (DNN)-based speech enhancement. Therefore, the front-end enhancement model can be optimized towards improving the accuracies of the back-end ASR system. Experiments on single-channel CHiME-4 Challenge show that CEGM yields consistently the highest correlations with word error rate (WER) which is often costly to calculate, and achieves the most accurate assessment of ASR performance when compared to the perceptual evaluation metrics commonly used for assessing speech enhancement performance. Furthermore, CEGM-optimized speech enhancement could effectively reduce the WER on the CHiME-4 real test set when compared to unprocessed noisy speech and enhanced speech obtained with MMSE-optimized enhancement for ASR systems with fixed multi-condition acoustic models in various deep architectures.

The multi-objective learning using minimum mean squared error criterion for DNN-based speech enhancement (MMSE-MOL-DNN) has been demonstrated to achieve better performance than single output DNN. However, one problem of MMSE-MOL-DNN is that the prediction error values on different targets have a very broad dynamic range, causing difficulty in DNN training. In this paper, we extend the maximum likelihood approach proposed in our previous work [1] to the multi-objective learning for DNN-based speech enhancement (ML-MOL-DNN) to achieve the automatic adjustment of the dynamic range of prediction error values on different targets. The conditional likelihood function to be maximized is derived under the generalized Gaussian distribution (GGD) error model. Moreover, the control of the dynamic range of the prediction error values on different targets is achieved by the scale factors in GGD. Furthermore, we propose a method to update the shape factors automatically utilizing the one-to-one mapping between the kurtosis and shape factor in GGD instead of manual adjustment. The experimental results show that our ML-MOL-DNN can achieve better performance than MMSE-MOL-DNN in terms of different objective measures.

In our previous work, a progressive learning framework for long short-term memory (LSTM)-based speech enhancement was proposed to improve the performance in low SNR environment, where each LSTM layer is guided to learn an intermediate target with a specific SNR gain via the MMSE criterion. However, the constraint relationship among these targets is not considered in the objective function. In this paper, we incorporate two kinds of geometric constraints among these targets into the objective function to help LSTM achieve better training. One constraint is edge constraint and the other is the centroid constraint. In addition, we propose a method for constructing the intermediate targets online. It saves device storage space and alleviates the trouble of manually constructing intermediate targets. Experiment results demonstrate these geometric constraints can bring remarkable improvements in low SNR environments.

From a statistical perspective, the conventional minimum mean squared error (MMSE) criterion can be considered as the maximum likelihood (ML) solution under an assumed homoscedastic Gaussian error model. However, in this paper, a statistical analysis reveals the super-Gaussian and heteroscedastic properties of the prediction errors in nonlinear regression deep neural network (DNN)-based speech enhancement when estimating clean log-power spectral (LPS) components at DNN outputs with noisy LPS features in DNN input vectors. Accordingly, we propose treating all dimensions of the prediction error vector as statistically independent random variables and model them with generalized Gaussian distributions (GGDs). Then, the objective function with the GGD error model is derived according to the ML criterion. Experiments on the TIMIT corpus corrupted by simulated additive noises show consistent improvements of our proposed DNN framework over the conventional DNN framework in terms of various objective quality measures under 14 unseen noise types evaluated and at various signal-to-noise ratio levels. Furthermore, the ML optimization objective with GGD outperforms the conventional MMSE criterion, achieving improved generalization and robustness.