Search results

Meet the SluBot team from UC Santa Cruz, a public land-grant research university in Santa Cruz, California.

Picture walking into your house after a long day. As you step in, your favorite music starts playing in the background and the lights dim to suit the weather outside.

We're an eclectic team of research-oriented undergraduate and graduate students in Princeton's CS and math departments.

Sounding Board is a conversational agent that engages users through thoughtful and informative discussion.

Meet the EmersonBot team from Emory University, a private research university in Atlanta, Georgia, US.

The UC Berkeley team consists of William, Phillip, Piyush, and James, and is the only fully undergraduate team in the competition.

This team, consisting of PhD and Masters students from Carnegie Mellon University, brings together experience in chatbot interaction strategies, question answering, neural modeling and machine learning.

We are the Alquist team from CTU, Prague, Czech Republic.

We are Edina, from the University of Edinburgh, a world-leading institution in Artificial Intelligence.

Our international team of 6 PhD students and faculty advisors has a wide range of experience from both academic and industrial research.

We are five graduate and undergraduate students of cognitive science, computer science, and applied physics from Rensselaer Polytechnic Institute.

Our team has been developed from a deep learning study group at SNU.

The DeisBot team is comprised of seven graduate students in the Computational Linguistics department at Brandeis University.

Several consumer speech devices feature voice interfaces that perform on-device keyword spotting to initiate user interactions. Accurate on-device keyword spotting within a tight CPU budget is crucial for such devices. Motivated by this, we investigated two ways to improve deep neural network (DNN) acoustic models for keyword spotting without increasing CPU usage. First, we used low-rank weight matrices

We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained network. A posterior smoothing based evaluation approach is employed to measure keyword spotting performance. Our experimental

We propose improved Deep Neural Network (DNN) training loss functions for more accurate single keyword spotting on resource-constrained embedded devices. The loss function modifications consist of a combination of multi-task training and weighted cross entropy. In the multi-task architecture, the keyword DNN acoustic model is trained with two tasks in parallel - the main task of predicting the keyword-specific

Producing large enough quantities of high-quality transcriptions for accurate and reliable evaluation of an automatic speech recognition (ASR) system can be costly. It is therefore desirable to minimize the manual transcription work for producing metrics with an agreed precision. In this paper we demonstrate how to improve ASR evaluation precision using stratified sampling. We show that by altering the

Voice search applications are typically evaluated by comparing the predicted query to a reference human transcript, regardless of the search results returned by the query. While we find that an exact transcript match is highly indicative of user satisfaction, a transcript which does not match the reference still produces satisfactory search results a significant fraction of the time. This paper therefore

We exhibit a foldable Extended Kalman Filter that internally integrates non-linear equations of motion with a nested fold of generic integrators over lazy streams in constant memory. Functional form allows us to switch integrators easily and to diagnose filter divergence accurately, achieving orders of magnitude better speed than the source example from the literature. As with all Kalman folds, we can move

The goal of this paper is to simulate the benefits of jointly applying active learning (AL) and semi-supervised training (SST) in a new speech recognition application. Our data selection approach relies on confidence filtering, and its impact on both the acoustic and language models (AM and LM) is studied. While AL is known to be beneficial to AM training, we show that it also carries out substantial improvements