102,867 research outputs found
Phonetic Temporal Neural Model for Language Identification
Deep neural models, particularly the LSTM-RNN model, have shown great
potential for language identification (LID). However, the use of phonetic
information has been largely overlooked by most existing neural LID methods,
although this information has been used very successfully in conventional
phonetic LID systems. We present a phonetic temporal neural model for LID,
which is an LSTM-RNN LID system that accepts phonetic features produced by a
phone-discriminative DNN as the input, rather than raw acoustic features. This
new model is similar to traditional phonetic LID methods, but the phonetic
knowledge here is much richer: it is at the frame level and involves compacted
information of all phones. Our experiments conducted on the Babel database and
the AP16-OLR database demonstrate that the temporal phonetic neural approach is
very effective, and significantly outperforms existing acoustic neural models.
It also outperforms the conventional i-vector approach on short utterances and
in noisy conditions.Comment: Submitted to TASL
On the smoothness of nonlinear system identification
We shed new light on the \textit{smoothness} of optimization problems arising
in prediction error parameter estimation of linear and nonlinear systems. We
show that for regions of the parameter space where the model is not
contractive, the Lipschitz constant and -smoothness of the objective
function might blow up exponentially with the simulation length, making it hard
to numerically find minima within those regions or, even, to escape from them.
In addition to providing theoretical understanding of this problem, this paper
also proposes the use of multiple shooting as a viable solution. The proposed
method minimizes the error between a prediction model and the observed values.
Rather than running the prediction model over the entire dataset, multiple
shooting splits the data into smaller subsets and runs the prediction model
over each subset, making the simulation length a design parameter and making it
possible to solve problems that would be infeasible using a standard approach.
The equivalence to the original problem is obtained by including constraints in
the optimization. The new method is illustrated by estimating the parameters of
nonlinear systems with chaotic or unstable behavior, as well as neural
networks. We also present a comparative analysis of the proposed method with
multi-step-ahead prediction error minimization
Look, Listen and Learn - A Multimodal LSTM for Speaker Identification
Speaker identification refers to the task of localizing the face of a person
who has the same identity as the ongoing voice in a video. This task not only
requires collective perception over both visual and auditory signals, the
robustness to handle severe quality degradations and unconstrained content
variations are also indispensable. In this paper, we describe a novel
multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies
both visual and auditory modalities from the beginning of each sequence input.
The key idea is to extend the conventional LSTM by not only sharing weights
across time steps, but also sharing weights across modalities. We show that
modeling the temporal dependency across face and voice can significantly
improve the robustness to content quality degradations and variations. We also
found that our multimodal LSTM is robustness to distractors, namely the
non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory
dataset and showed that our system outperforms the state-of-the-art systems in
speaker identification with lower false alarm rate and higher recognition
accuracy.Comment: The 30th AAAI Conference on Artificial Intelligence (AAAI-16
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Recurrent neural networks (RNNs) are a vital modeling technique that rely on
internal states learned indirectly by optimization of a supervised,
unsupervised, or reinforcement training loss. RNNs are used to model dynamic
processes that are characterized by underlying latent states whose form is
often unknown, precluding its analytic representation inside an RNN. In the
Predictive-State Representation (PSR) literature, latent state processes are
modeled by an internal state representation that directly models the
distribution of future observations, and most recent work in this area has
relied on explicitly representing and targeting sufficient statistics of this
probability distribution. We seek to combine the advantages of RNNs and PSRs by
augmenting existing state-of-the-art recurrent neural networks with
Predictive-State Decoders (PSDs), which add supervision to the network's
internal state representation to target predicting future observations.
Predictive-State Decoders are simple to implement and easily incorporated into
existing training pipelines via additional loss regularization. We demonstrate
the effectiveness of PSDs with experimental results in three different domains:
probabilistic filtering, Imitation Learning, and Reinforcement Learning. In
each, our method improves statistical performance of state-of-the-art recurrent
baselines and does so with fewer iterations and less data.Comment: NIPS 201
Phone-aware Neural Language Identification
Pure acoustic neural models, particularly the LSTM-RNN model, have shown
great potential in language identification (LID). However, the phonetic
information has been largely overlooked by most of existing neural LID models,
although this information has been used in the conventional phonetic LID
systems with a great success. We present a phone-aware neural LID architecture,
which is a deep LSTM-RNN LID system but accepts output from an RNN-based ASR
system. By utilizing the phonetic knowledge, the LID performance can be
significantly improved. Interestingly, even if the test language is not
involved in the ASR training, the phonetic knowledge still presents a large
contribution. Our experiments conducted on four languages within the Babel
corpus demonstrated that the phone-aware approach is highly effective.Comment: arXiv admin note: text overlap with arXiv:1705.0315
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