Low latency modeling of temporal contexts for speech recognition

This thesis focuses on the development of neural network acoustic models for large vocabulary continuous speech recognition (LVCSR) to satisfy the design goals of low latency and low computational complexity. Low latency enables online speech recognition; and low computational complexity helps reduce the computational cost both during training and inference. Long span sequential dependencies and sequential distortions in the input vector sequence are a major challenge in acoustic modeling. Recurrent neural networks have been shown to effectively model these dependencies. Specifically, bidirectional long short term memory (BLSTM) networks, provide state-of-the-art performance across several LVCSR tasks. However the deployment of bidirectional models for online LVCSR is non-trivial due to their large latency; and unidirectional LSTM models are typically preferred. In this thesis we explore the use of hierarchical temporal convolution to model long span temporal dependencies. We propose a sub-sampled variant of these temporal convolution neural networks, termed time-delay neural networks (TDNNs). These sub-sampled TDNNs reduce the computation complexity by ~5x, compared to TDNNs, during frame randomized pre-training. These models are shown to be effective in modeling long-span temporal contexts, however there is a performance gap compared to (B)LSTMs. As recent advancements in acoustic model training have eliminated the need for frame randomized pre-training we modify the TDNN architecture to use higher sampling rates, as the increased computation can be amortized over the sequence. These variants of sub- sampled TDNNs provide performance superior to unidirectional LSTM networks, while also affording a lower real time factor (RTF) during inference. However we show that the BLSTM models outperform both the TDNN and LSTM models. We propose a hybrid architecture interleaving temporal convolution and LSTM layers which is shown to outperform the BLSTM models. Further we improve these BLSTM models by using higher frame rates at lower layers and show that the proposed TDNN- LSTM model performs similar to these superior BLSTM models, while reducing the overall latency to 200 ms. Finally we describe an online system for reverberation robust ASR, using the above described models in conjunction with other data augmentation techniques like reverberation simulation, which simulates far-field environments, and volume perturbation, which helps tackle volume variation even without gain normalization

Graph-Based Machine Learning for Passive Network Reconnaissance within Encrypted Networks

Network reconnaissance identifies a network’s vulnerabilities to both prevent and mitigate the impact of cyber-attacks. The difficulty of performing adequate network reconnaissance has been exacerbated by the rising complexity of modern networks (e.g., encryption). We identify that the majority of network reconnaissance solutions proposed in literature are infeasible for widespread deployment in realistic modern networks. This thesis provides novel network reconnaissance solutions to address the limitations of the existing conventional approaches proposed in literature. The existing approaches are limited by their reliance on large, heterogeneous feature sets making them difficult to deploy under realistic network conditions. In contrast, we devise a bipartite graph-based representation to create network reconnaissance solutions that rely only on a single feature (e.g., the Internet protocol (IP) address field). We exploit a widely available feature set to provide network reconnaissance solutions that are scalable, independent of encryption, and deployable across diverse Internet (TCP/IP) networks. We design bipartite graph embeddings (BGE); a graph-based machine learning (ML) technique for extracting insight from the structural properties of the bipartite graph-based representation. BGE is the first known graph embedding technique designed explicitly for network reconnaissance. We validate the use of BGE through an evaluation of a university’s enterprise network. BGE is shown to provide insight into crucial areas of network reconnaissance (e.g., device characterisation, service prediction, and network visualisation). We design an extension of BGE to acquire insight within a private network. Private networks—such as a virtual private network (VPN)—have posed significant challenges for network reconnaissance as they deny direct visibility into their composition. Our extension of BGE provides the first known solution for inferring the composition of both the devices and applications acting behind diverse private networks. This thesis provides novel graph-based ML techniques for two crucial aims of network reconnaissance—device characterisation and intrusion detection. The techniques developed within this thesis provide unique cybersecurity solutions to both prevent and mitigate the impact of cyber-attacks.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering , 202

Distant Speech Recognition of Natural Spontaneous Multi-party Conversations

Distant speech recognition (DSR) has gained wide interest recently. While deep networks keep improving ASR overall, the performance gap remains between using close-talking recordings and distant recordings. Therefore the work in this thesis aims at providing some insights for further improvement of DSR performance. The investigation starts with collecting the first multi-microphone and multi-media corpus of natural spontaneous multi-party conversations in native English with the speaker location tracked, i.e. the Sheffield Wargame Corpus (SWC). The state-of-the-art recognition systems with the acoustic models trained standalone and adapted both show word error rates (WERs) above 40% on headset recordings and above 70% on distant recordings. A comparison between SWC and AMI corpus suggests a few unique properties in the real natural spontaneous conversations, e.g. the very short utterances and the emotional speech. Further experimental analysis based on simulated data and real data quantifies the impact of such influence factors on DSR performance, and illustrates the complex interaction among multiple factors which makes the treatment of each influence factor much more difficult. The reverberation factor is studied further. It is shown that the reverberation effect on speech features could be accurately modelled with a temporal convolution in the complex spectrogram domain. Based on that a polynomial reverberation score is proposed to measure the distortion level of short utterances. Compared to existing reverberation metrics like C50, it avoids a rigid early-late-reverberation partition without compromising the performance on ranking the reverberation level of recording environments and channels. Furthermore, the existing reverberation measurement is signal independent thus unable to accurately estimate the reverberation distortion level in short recordings. Inspired by the phonetic analysis on the reverberation distortion via self-masking and overlap-masking, a novel partition of reverberation distortion into the intra-phone smearing and the inter-phone smearing is proposed, so that the reverberation distortion level is first estimated on each part and then combined