Advances in All-Neural Speech Recognition

This paper advances the design of CTC-based all-neural (or end-to-end) speech recognizers. We propose a novel symbol inventory, and a novel iterated-CTC method in which a second system is used to transform a noisy initial output into a cleaner version. We present a number of stabilization and initialization methods we have found useful in training these networks. We evaluate our system on the commonly used NIST 2000 conversational telephony test set, and significantly exceed the previously published performance of similar systems, both with and without the use of an external language model and decoding technology

The Microsoft 2017 Conversational Speech Recognition System

We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art on the Switchboard speech recognition task. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby subsets of acoustic models are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added a confusion network rescoring step after system combination. The resulting system yields a 5.1\% word error rate on the 2000 Switchboard evaluation set

The Microsoft 2016 Conversational Speech Recognition System

We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task

Adversarial Reweighting for Speaker Verification Fairness

We address performance fairness for speaker verification using the adversarial reweighting (ARW) method. ARW is reformulated for speaker verification with metric learning, and shown to improve results across different subgroups of gender and nationality, without requiring annotation of subgroups in the training data. An adversarial network learns a weight for each training sample in the batch so that the main learner is forced to focus on poorly performing instances. Using a min-max optimization algorithm, this method improves overall speaker verification fairness. We present three different ARWformulations: accumulated pairwise similarity, pseudo-labeling, and pairwise weighting, and measure their performance in terms of equal error rate (EER) on the VoxCeleb corpus. Results show that the pairwise weighting method can achieve 1.08% overall EER, 1.25% for male and 0.67% for female speakers, with relative EER reductions of 7.7%, 10.1% and 3.0%, respectively. For nationality subgroups, the proposed algorithm showed 1.04% EER for US speakers, 0.76% for UK speakers, and 1.22% for all others. The absolute EER gap between gender groups was reduced from 0.70% to 0.58%, while the standard deviation over nationality groups decreased from 0.21 to 0.19

Development of novel 2D and 3D correlative microscopy to characterise the composition and multiscale structure of suspended sediment aggregates.

Suspended cohesive sediments form aggregates or 'flocs' and are often closely associated with carbo, nutrients, pathogens and pollutants, which makes understanding their composition, transport and fate highly desirable. Accurate prediction of floc behaviour requires the quantification of 3-dimensional (3D) properties (size, shoe and internal structure) that span several scales (i.e. nanometre [nm] to millimetre [mm]-scale). Traditional techniques (optical cameras and electron microscopy [EM]), however, can only provide 2-dimensional (2D) simplifications of 3D floc geometries. Additionally, the existence of a resolution gap between conventional optical microscopy (COM) and transmission EM (TEM) prevents an understanding of how floc nm-scale constituents and internal structure influence mm-scale floc properties. Here, we develop a novel correlative imaging workflow combining 3D X-ray micro-computed tomography (μCT), 3D focused ion beam nanotomography (FIB-nt) and 2D scanning EM (SEM) and TEM (STEM) which allows us to stabilise, visualise and quantify the composition and multi scale structure of sediment flocs for the first time. This new technique allowed the quantification of 3D floc geometries, the identification of individual floc components (e.g., clays, non-clay minerals and bacteria), and characterisation of particle-particle and structural associations across scales. This novel dataset demonstrates the truly complex structure of natural flocs at multiple scales. The integration of multiscale, state-of-the-art instrumentation/techniques offers the potential to generate fundamental new understanding of floc composition, structure and behaviour

THE MICROSOFT 2016 CONVERSATIONAL SPEECH RECOGNITION SYSTEM

ABSTRACT We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task

Hydrodynamic coupling in microbially mediated fracture mineralization : formation of self-organized groundwater flow channels

Evidence of fossilized microorganisms embedded within mineral veins and mineral-filled fractures has been observed in a wide range of geological environments. Microorganisms can act as sites for mineral nucleation and also contribute to mineral precipitation by inducing local geochemical changes. In this study, we explore fundamental controls on microbially induced mineralization in rock fractures. Specifically, we systematically investigate the influence of hydrodynamics (velocity, flow rate, aperture) on microbially mediated calcite precipitation. Our experimental results demonstrate that a feedback mechanism exists between the gradual reduction in fracture aperture due to precipitation, and its effect on the local fluid velocity. This feedback results in mineral fill distributions that focus flow into a small number of self-organizing channels that remain open, ultimately controlling the final aperture profile that governs flow within the fracture. This hydrodynamic coupling can explain field observations of discrete groundwater flow channeling within fracture-fill mineral geometries where strong evidence of microbial activity is reported