Automatic Speech Recognizer (ASR) and Natural Language Understanding (NLU) are the two key components for any voice assistant. ASR converts the input audio signal to text using acoustic model (AM), language model (LM) and Decoder. NLU further processes this text for sub-tasks like predicting domain, intent and slots. Since input to NLU is text, any error in ASR module will propagate in NLU sub-tasks. ASR generally process speech in small duration windows and first generates phonemes using Acoustic Model (AM) and then Word Lattices using Decoder, Dictionary and Language Model (LM). Training and maintaining a generic LM, which fits the distribution of data of multiple domains is a difficult task. So our proposed architecture uses multiple domain specific LMs to rescore word lattice and has a way to select LMs for rescoring. In this paper, we are proposing a novel Multistage CNN architecture to classify the domain from partial phoneme sequence and use it to select top K domain LMs. The accuracy of multistage classification model based on phoneme input for top three domains has achieved state-of-the-art results on 2 open datasets, 97.76% in ATIS and 99.57% in Snips.

Transfer Learning (TL) plays a crucial role when a given dataset has insufficient labeled examples to train an accurate model. In such scenarios, the knowledge accumulated within a model pre-trained on a source dataset can be transferred to a target dataset, resulting in the improvement of the target model. Though TL is found to be successful in the realm of image-based applications, its impact and practical use in Natural Language Processing (NLP) applications is still a subject of research. Due to their hierarchical architecture, Deep Neural Networks (DNN) provide flexibility and customization in adjusting their parameters and depth of layers, thereby forming an apt area for exploiting the use of TL. In this paper, we report the results and conclusions obtained from extensive empirical experiments using a Convolutional Neural Network (CNN) and try to uncover thumb rules to ensure a successful positive transfer. In addition, we also highlight the flawed means that could lead to a negative transfer. We explore the transferability of various layers and describe the effect of varying hyper-parameters on the transfer performance. Also, we present a comparison of accuracy value and model size against state-of-the-art methods. Finally, we derive inferences from the empirical results and provide best practices to achieve a successful positive transfer.

Local storage is required in many sensor network applications, both for archival of detailed event information, as well as to overcome sensor platform memory constraints. Recent gains in energy efficiency of new-generation NAND flash storage have strengthened the case for in-network storage by data-centric sensor network applications. We argue that current storage solutions offering a simple file system abstraction are inadequate for sensor applications to exploit storage. Instead, we propose Capsule—a rich, flexible and portable object storage abstraction that offers stream, file, array, queue and index storage objects for data storage and retrieval. Further, Capsule supports checkpointing and rollback of object state for fault tolerance. Our experiments demonstrate that Capsule provides platform independence, greater functionality and greater energy efficiency than existing storage solutions.

Local storage is required in many sensor network applications, both for archival of detailed event information, as well as to overcome sensor platform memory constraints. While extensive measurement studies have been performed to highlight the trade-off between computation and communication in sensor networks, the role of storage has received little attention. The storage subsystems on currently available sensor platforms have not exploited technology trends, and consequently the energy cost of storage on these platforms is as high as that of communication. Current flash memories, however, offer a low-priced, high-capacity and extremely energy-efficient storage solution.In this paper, we perform a comprehensive evaluation of the active and sleep-mode energy consumption of available flash-based storage options for sensor platforms. Our results demonstrate more than a 100-fold decrease in per-byte energy consumption for surface-mount parallel NAND flash in comparison with the MicaZ on-board serial flash. In addition, this dramatically reduces storage energy costs relative to communication, introducing a new dimension in traditional computation vs communication trade-offs. Our results have significant ramifications on the design of sensor platforms as well as on the energy consumption of sensing applications. We quantify the potential energy gains for two commonly used sensor network services: communication and in-network data aggregation. Our measurements show significant improvements in each service: 50-fold and up to 10-fold reductions in energy for communication and data aggregation respectively.

Improved energy-efficiency and storage capacity of new-generation NAND flash memory makes a compelling case for storage-centric sensor networks. Such a storage-centric sensor network emphasizes the use of platforms with larger storage and more extensive use of the storage capacities on sensors. We demonstrate the feasibility of storage-centric sensor networks using an instance of a storage-centric camera sensor network that is more energy-efficient in comparison to a traditional camera sensor network. We demonstrate multiple camera sensors, each consisting of a Cyclops camera attached to a MicaZ mote, using motion-triggered image capturing. The captured images are archived locally on flash storage and summaries of detected events are transmitted to the base-station. The base-station picks the events of interest from the summaries and requests the original captured image from the sensor as required. The use of high-capacity energy-efficient flash storage at the sensor allows us to trade-off expensive radio communication for cheaper local storage, improving the life-time of the battery and consequently, the life of the storage-centric camera sensor network.

Recent gains in energy-efficiency of new-generation NAND flash storage have strengthened the case for in-network storage by data-centric sensor network applications. This paper argues that a simple file system abstraction is inadequate for realizing the full benefits of high-capacity lowpower NAND flash storage in data-centric applications. Instead we advocate a rich object storage abstraction to support flexible use of the storage system for a variety of application needs and one that is specifically optimized for memory and energy-constrained sensor platforms. We propose Capsule, an energy-optimized log-structured object storage system for flash memories that enables sensor applications to exploit storage resources in a multitude of ways. Capsule employs a hardware abstraction layer that hides the vagaries of flash memories for the application and supports energy-optimized implementations of commonly used storage objects such as streams, files, arrays, queues and lists. Further, Capsule supports checkpointing and rollback of object states to tolerate software faults in sensor applications running on inexpensive, unreliable hardware. Our experiments demonstrate that Capsule provides platform-independence, greater functionality, more tunability, and greater energy-efficiency than existing sensor storage solutions, while operating even within the memory constraints of the Mica2 Mote. Our experiments not only demonstrate the energy and memory-efficiency of I/O operations in Capsule but also shows that Capsule consumes less than 15% of the total energy cost in a typical sensor application.