9,669 research outputs found
Improved training of end-to-end attention models for speech recognition
Sequence-to-sequence attention-based models on subword units allow simple
open-vocabulary end-to-end speech recognition. In this work, we show that such
models can achieve competitive results on the Switchboard 300h and LibriSpeech
1000h tasks. In particular, we report the state-of-the-art word error rates
(WER) of 3.54% on the dev-clean and 3.82% on the test-clean evaluation subsets
of LibriSpeech. We introduce a new pretraining scheme by starting with a high
time reduction factor and lowering it during training, which is crucial both
for convergence and final performance. In some experiments, we also use an
auxiliary CTC loss function to help the convergence. In addition, we train long
short-term memory (LSTM) language models on subword units. By shallow fusion,
we report up to 27% relative improvements in WER over the attention baseline
without a language model.Comment: submitted to Interspeech 201
Modularity and Neural Integration in Large-Vocabulary Continuous Speech Recognition
This Thesis tackles the problems of modularity in Large-Vocabulary Continuous Speech Recognition with use of Neural Network
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Neural models have become ubiquitous in automatic speech recognition systems.
While neural networks are typically used as acoustic models in more complex
systems, recent studies have explored end-to-end speech recognition systems
based on neural networks, which can be trained to directly predict text from
input acoustic features. Although such systems are conceptually elegant and
simpler than traditional systems, it is less obvious how to interpret the
trained models. In this work, we analyze the speech representations learned by
a deep end-to-end model that is based on convolutional and recurrent layers,
and trained with a connectionist temporal classification (CTC) loss. We use a
pre-trained model to generate frame-level features which are given to a
classifier that is trained on frame classification into phones. We evaluate
representations from different layers of the deep model and compare their
quality for predicting phone labels. Our experiments shed light on important
aspects of the end-to-end model such as layer depth, model complexity, and
other design choices.Comment: NIPS 201
TheanoLM - An Extensible Toolkit for Neural Network Language Modeling
We present a new tool for training neural network language models (NNLMs),
scoring sentences, and generating text. The tool has been written using Python
library Theano, which allows researcher to easily extend it and tune any aspect
of the training process. Regardless of the flexibility, Theano is able to
generate extremely fast native code that can utilize a GPU or multiple CPU
cores in order to parallelize the heavy numerical computations. The tool has
been evaluated in difficult Finnish and English conversational speech
recognition tasks, and significant improvement was obtained over our best
back-off n-gram models. The results that we obtained in the Finnish task were
compared to those from existing RNNLM and RWTHLM toolkits, and found to be as
good or better, while training times were an order of magnitude shorter
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