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Amazon Aurora is a high-throughput cloud-native relational database offered as part of Amazon Web Services (AWS). One of the more novel differences between Aurora and other relational databases is how it pushes redo processing to a multi-tenant scale-out storage service, purpose-built for Aurora. Doing so reduces networking traffic, avoids checkpoints and crash recovery, enables failovers to replicas without

Linear models have been widely used in the industry for their low computation time, small memory footprint and interpretability. However, linear models are not capable of leveraging non-linear feature interactions in predicting the target. This limits their performance. A classical approach to overcome this limitation is to use combinations of the original features, referred to as higher-order features,

We present an end-of-utterance detector for real-time automatic speech recognition in far-field scenarios. The proposed system consists of three components: a long short-term memory (LSTM) neural network trained on acoustic features, an LSTM trained on 1-best recognition hypotheses of the automatic speech recognition (ASR) decoder, and a feedforward deep neural network (DNN) combining embeddings derived

In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (IPDAs). This scenario is observed for many mainstream IPDAs in industry that allow third parties to develop thousands of new domains to augment built-in ones to rapidly increase domain coverage and overall IPDA capabilities. We

Pitch detection is a fundamental problem in speech processing as F0 is used in a large number of applications. Recent papers have proposed deep learning for robust pitch tracking. In this letter, we consider voicing detection as a classification problem and F0 contour estimation as a regression problem. For both tasks, acoustic features from multiple domains and traditional machine learning methods are

In task-oriented dialog, agents need to generate both fluent natural language responses and correct external actions like database queries and updates. We show that methods that achieve state of the art performance on synthetic datasets, perform poorly in real world dialog tasks. We propose a hybrid model, where nearest neighbor is used to generate fluent responses and Sequence-to-Sequence (Seq2Seq) type

Phrasing structure is one of the most important factors in increasing the naturalness of text-to-speech (TTS) systems, in particular for long-form reading. Most existing TTS systems are optimized for isolated short sentences, and completely discard the larger context or structure of the text. This paper presents how we have built phrasing models based on data extracted from audiobooks. We investigate how

We present a neural network based approach to two-channel beamforming. First, single- and cross-channel spectral features are extracted to form a feature map for each utterance. A large neural network that is the concatenation of a convolution neural network (CNN), long short-term memory recurrent neural network (LSTMRNN) and deep neural network (DNN) is then employed to estimate frame-level speech and

Accurate on-device wake word detection is crucial to products with far-field voice control such as the Amazon Echo. It is quite challenging to build a wake word system with both low False Reject Rate (FRR) and low False Alarm Rate (FAR) in real scenarios where there are various types of background speech, music or noise, especially when computational resources on the device is limited. In this paper, we

We propose a novel information-theoretic approach for evaluating microphone arrays that relies on the array physics and geometry rather than the underlying beamforming algorithm. The analogy between Multiple-Input-Multiple-Output (MIMO) wireless communication channel and the acoustic channel of microphone arrays is exploited to define information measures of microphone arrays, which provide upper bounds

We propose a simple recurrent model for detecting rare sound events, when the time boundaries of events are available for training. Our model optimizes the combination of an utterancelevel loss, which classifies whether an event occurs in an utterance, and a frame-level loss, which classifies whether each frame corresponds to the event when it does occur. The two losses make use of a shared vectorial representation

This paper proposes an efficient real-time multirate fast transient-sound detection algorithm on the basis of emerging microphone array configuration intended for multimedia signal processing application systems such as digital smart home. The proposed detection algorithm first extracts the dynamics and periodicity features, then trains the model parameters of these features on Amazon machine learning platform

Multimedia streaming services over spoken dialog systems have become ubiquitous. User-entity affinity modeling is critical for the system to understand and disambiguate user intents and personalize user experiences. However, fully voice-based interaction demands quantification of novel behavioral cues to determine user affinities. In this work, we propose using play duration cues to learn a matrix factorization

This paper proposes a Region-based Convolutional Recurrent Neural Network (R-CRNN) for audio event detection (AED). The proposed network is inspired by Faster-RCNN [1], a wellknown region-based convolutional network framework for visual object detection. Different from the original Faster-RCNN, a recurrent layer is added on top of the convolutional network to capture the long-term temporal context from

Recurrent Neural Networks (RNN) have recently proved to be effective in acoustic modeling for TTS. Various techniques such as the Maximum Likelihood Parameter Generation (MLPG) algorithm have been naturally inherited from the HMM-based speech synthesis framework. This paper investigates in which situations parameter generation and variance restoration approaches help for RNN-based TTS. We explore how their

Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs. Small amounts of noise can destroy the performance of an otherwise state-of-the-art model. To harden models against background noise, practitioners often perform data augmentation, adding artificially-noised examples to the training set, carrying over the original label. In this paper

We describe an intelligent context-aware conversational system that incorporates screen context information to service multimodal user requests. Screen content is used for disambiguation of utterances that refer to screen objects and for enabling the user to act upon screen objects using voice commands. We propose a deep learning architecture that jointly models the user utterance and the screen and incorporates

We propose a framework that harnesses visual cues in an unsupervised manner to learn the co-occurrence distribution of items in real-world images for complementary recommendation. Our model learns a non-linear transformation between the two manifolds of source and target item categories (e.g., tops and bottoms in outfits). Given a large dataset of images containing instances of co-occurring items, we train

We consider the task of predicting subjective fashion traits from images using neural networks. Specifically, we are interested in training a network for ranking outfits according to how well they fit the user. In order to capture the variability induced by human subjective considerations, each training example is annotated by a panel of fashion experts. Similarly to previous works on subjective data, the

In this paper we investigate statistical model compression applied to natural language understanding (NLU) models. Small-footprint NLU models are important for enabling offline systems on hardware restricted devices, and for decreasing on demand model loading latency in cloud-based systems. To compress NLU models, we present two main techniques, parameter quantization and perfect feature hashing. These