70,305 research outputs found
System Identification with Time-Aware Neural Sequence Models
Established recurrent neural networks are well-suited to solve a wide variety
of prediction tasks involving discrete sequences. However, they do not perform
as well in the task of dynamical system identification, when dealing with
observations from continuous variables that are unevenly sampled in time, for
example due to missing observations. We show how such neural sequence models
can be adapted to deal with variable step sizes in a natural way. In
particular, we introduce a time-aware and stationary extension of existing
models (including the Gated Recurrent Unit) that allows them to deal with
unevenly sampled system observations by adapting to the observation times,
while facilitating higher-order temporal behavior. We discuss the properties
and demonstrate the validity of the proposed approach, based on samples from
two industrial input/output processes.Comment: 34th AAAI Conference on Artificial Intelligence (AAAI 2020
Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System
In this paper, we explore the encoding/pooling layer and loss function in the
end-to-end speaker and language recognition system. First, a unified and
interpretable end-to-end system for both speaker and language recognition is
developed. It accepts variable-length input and produces an utterance level
result. In the end-to-end system, the encoding layer plays a role in
aggregating the variable-length input sequence into an utterance level
representation. Besides the basic temporal average pooling, we introduce a
self-attentive pooling layer and a learnable dictionary encoding layer to get
the utterance level representation. In terms of loss function for open-set
speaker verification, to get more discriminative speaker embedding, center loss
and angular softmax loss is introduced in the end-to-end system. Experimental
results on Voxceleb and NIST LRE 07 datasets show that the performance of
end-to-end learning system could be significantly improved by the proposed
encoding layer and loss function.Comment: Accepted for Speaker Odyssey 201
Hierarchical Character-Word Models for Language Identification
Social media messages' brevity and unconventional spelling pose a challenge
to language identification. We introduce a hierarchical model that learns
character and contextualized word-level representations for language
identification. Our method performs well against strong base- lines, and can
also reveal code-switching
- …