26,133 research outputs found
Character-Level Incremental Speech Recognition with Recurrent Neural Networks
In real-time speech recognition applications, the latency is an important
issue. We have developed a character-level incremental speech recognition (ISR)
system that responds quickly even during the speech, where the hypotheses are
gradually improved while the speaking proceeds. The algorithm employs a
speech-to-character unidirectional recurrent neural network (RNN), which is
end-to-end trained with connectionist temporal classification (CTC), and an
RNN-based character-level language model (LM). The output values of the
CTC-trained RNN are character-level probabilities, which are processed by beam
search decoding. The RNN LM augments the decoding by providing long-term
dependency information. We propose tree-based online beam search with
additional depth-pruning, which enables the system to process infinitely long
input speech with low latency. This system not only responds quickly on speech
but also can dictate out-of-vocabulary (OOV) words according to pronunciation.
The proposed model achieves the word error rate (WER) of 8.90% on the Wall
Street Journal (WSJ) Nov'92 20K evaluation set when trained on the WSJ SI-284
training set.Comment: To appear in ICASSP 201
Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks
Deep neural networks (DNNs) have become a widely deployed model for numerous
machine learning applications. However, their fixed architecture, substantial
training cost, and significant model redundancy make it difficult to
efficiently update them to accommodate previously unseen data. To solve these
problems, we propose an incremental learning framework based on a
grow-and-prune neural network synthesis paradigm. When new data arrive, the
neural network first grows new connections based on the gradients to increase
the network capacity to accommodate new data. Then, the framework iteratively
prunes away connections based on the magnitude of weights to enhance network
compactness, and hence recover efficiency. Finally, the model rests at a
lightweight DNN that is both ready for inference and suitable for future
grow-and-prune updates. The proposed framework improves accuracy, shrinks
network size, and significantly reduces the additional training cost for
incoming data compared to conventional approaches, such as training from
scratch and network fine-tuning. For the LeNet-300-100 and LeNet-5 neural
network architectures derived for the MNIST dataset, the framework reduces
training cost by up to 64% (63%) and 67% (63%) compared to training from
scratch (network fine-tuning), respectively. For the ResNet-18 architecture
derived for the ImageNet dataset and DeepSpeech2 for the AN4 dataset, the
corresponding training cost reductions against training from scratch (network
fine-tunning) are 64% (60%) and 67% (62%), respectively. Our derived models
contain fewer network parameters but achieve higher accuracy relative to
conventional baselines
Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection
Encoder-decoder models provide a generic architecture for
sequence-to-sequence tasks such as speech recognition and translation. While
offline systems are often evaluated on quality metrics like word error rates
(WER) and BLEU, latency is also a crucial factor in many practical use-cases.
We propose three latency reduction techniques for chunk-based incremental
inference and evaluate their efficiency in terms of accuracy-latency trade-off.
On the 300-hour How2 dataset, we reduce latency by 83% to 0.8 second by
sacrificing 1% WER (6% rel.) compared to offline transcription. Although our
experiments use the Transformer, the hypothesis selection strategies are
applicable to other encoder-decoder models. To avoid expensive re-computation,
we use a unidirectionally-attending encoder. After an adaptation procedure to
partial sequences, the unidirectional model performs on-par with the original
model. We further show that our approach is also applicable to low-latency
speech translation. On How2 English-Portuguese speech translation, we reduce
latency to 0.7 second (-84% rel.) while incurring a loss of 2.4 BLEU points (5%
rel.) compared to the offline system
Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction
Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659
Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction
British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225
Incremental LSTM-based Dialog State Tracker
A dialog state tracker is an important component in modern spoken dialog
systems. We present an incremental dialog state tracker, based on LSTM
networks. It directly uses automatic speech recognition hypotheses to track the
state. We also present the key non-standard aspects of the model that bring its
performance close to the state-of-the-art and experimentally analyze their
contribution: including the ASR confidence scores, abstracting scarcely
represented values, including transcriptions in the training data, and model
averaging
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