541 research outputs found
Recurrent Neural Network Training with Dark Knowledge Transfer
Recurrent neural networks (RNNs), particularly long short-term memory (LSTM),
have gained much attention in automatic speech recognition (ASR). Although some
successful stories have been reported, training RNNs remains highly
challenging, especially with limited training data. Recent research found that
a well-trained model can be used as a teacher to train other child models, by
using the predictions generated by the teacher model as supervision. This
knowledge transfer learning has been employed to train simple neural nets with
a complex one, so that the final performance can reach a level that is
infeasible to obtain by regular training. In this paper, we employ the
knowledge transfer learning approach to train RNNs (precisely LSTM) using a
deep neural network (DNN) model as the teacher. This is different from most of
the existing research on knowledge transfer learning, since the teacher (DNN)
is assumed to be weaker than the child (RNN); however, our experiments on an
ASR task showed that it works fairly well: without applying any tricks on the
learning scheme, this approach can train RNNs successfully even with limited
training data.Comment: ICASSP 201
Quantum filtering for multiple measurements driven by fields in single-photon states
In this paper, we derive the stochastic master equations for quantum systems
driven by a single-photon input state which is contaminated by quantum vacuum
noise. To improve estimation performance, quantum filters based on
multiple-channel measurements are designed. Two cases, namely diffusive plus
Poissonian measurements and two diffusive measurements, are considered.Comment: 8 pages, 6 figures, submitted for publication. Comments are welcome
Full-info Training for Deep Speaker Feature Learning
In recent studies, it has shown that speaker patterns can be learned from
very short speech segments (e.g., 0.3 seconds) by a carefully designed
convolutional & time-delay deep neural network (CT-DNN) model. By enforcing the
model to discriminate the speakers in the training data, frame-level speaker
features can be derived from the last hidden layer. In spite of its good
performance, a potential problem of the present model is that it involves a
parametric classifier, i.e., the last affine layer, which may consume some
discriminative knowledge, thus leading to `information leak' for the feature
learning. This paper presents a full-info training approach that discards the
parametric classifier and enforces all the discriminative knowledge learned by
the feature net. Our experiments on the Fisher database demonstrate that this
new training scheme can produce more coherent features, leading to consistent
and notable performance improvement on the speaker verification task.Comment: Accepted by ICASSP 201
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
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
Deep Speaker Feature Learning for Text-independent Speaker Verification
Recently deep neural networks (DNNs) have been used to learn speaker
features. However, the quality of the learned features is not sufficiently
good, so a complex back-end model, either neural or probabilistic, has to be
used to address the residual uncertainty when applied to speaker verification,
just as with raw features. This paper presents a convolutional time-delay deep
neural network structure (CT-DNN) for speaker feature learning. Our
experimental results on the Fisher database demonstrated that this CT-DNN can
produce high-quality speaker features: even with a single feature (0.3 seconds
including the context), the EER can be as low as 7.68%. This effectively
confirmed that the speaker trait is largely a deterministic short-time property
rather than a long-time distributional pattern, and therefore can be extracted
from just dozens of frames.Comment: deep neural networks, speaker verification, speaker featur
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