353 research outputs found
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
Deep LSTM is an ideal candidate for text recognition. However text
recognition involves some initial image processing steps like segmentation of
lines and words which can induce error to the recognition system. Without
segmentation, learning very long range context is difficult and becomes
computationally intractable. Therefore, alternative soft decisions are needed
at the pre-processing level. This paper proposes a hybrid text recognizer using
a deep recurrent neural network with multiple layers of abstraction and long
range context along with a language model to verify the performance of the deep
neural network. In this paper we construct a multi-hypotheses tree architecture
with candidate segments of line sequences from different segmentation
algorithms at its different branches. The deep neural network is trained on
perfectly segmented data and tests each of the candidate segments, generating
unicode sequences. In the verification step, these unicode sequences are
validated using a sub-string match with the language model and best first
search is used to find the best possible combination of alternative hypothesis
from the tree structure. Thus the verification framework using language models
eliminates wrong segmentation outputs and filters recognition errors
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation
We present state-of-the-art automatic speech recognition (ASR) systems
employing a standard hybrid DNN/HMM architecture compared to an attention-based
encoder-decoder design for the LibriSpeech task. Detailed descriptions of the
system development, including model design, pretraining schemes, training
schedules, and optimization approaches are provided for both system
architectures. Both hybrid DNN/HMM and attention-based systems employ
bi-directional LSTMs for acoustic modeling/encoding. For language modeling, we
employ both LSTM and Transformer based architectures. All our systems are built
using RWTHs open-source toolkits RASR and RETURNN. To the best knowledge of the
authors, the results obtained when training on the full LibriSpeech training
set, are the best published currently, both for the hybrid DNN/HMM and the
attention-based systems. Our single hybrid system even outperforms previous
results obtained from combining eight single systems. Our comparison shows that
on the LibriSpeech 960h task, the hybrid DNN/HMM system outperforms the
attention-based system by 15% relative on the clean and 40% relative on the
other test sets in terms of word error rate. Moreover, experiments on a reduced
100h-subset of the LibriSpeech training corpus even show a more pronounced
margin between the hybrid DNN/HMM and attention-based architectures.Comment: Proceedings of INTERSPEECH 201
Recognition of Cursive Arabic Handwritten Text using Embedded Training based on HMMs
In this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models HMMs The system is analytical without explicit segmentation used embedded training to perform and enhance the character models Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image These features are modelled using hidden Markov models and trained by embedded training The experiments on images of the benchmark IFN ENIT database show that the proposed system improves recognitio
Subword-based Stochastic Segment Modeling for Offline Arabic Handwriting Recognition
In this paper, we describe several experiments in which we use a stochastic segment model (SSM) to improve offline handwriting recognition (OHR) performance. We use the SSM to re-rank (re-score) multiple decoder hypotheses. Then, a probabilistic multi-class SVM is trained to model stochastic segments obtained from force aligning transcriptions with the underlying image. We extract multiple features from the stochastic segments that are sensitive to larger context span to train the SVM. Our experiments show that using confidence scores from the trained SVM within the SSM framework can significantly improve OHR performance. We also show that OHR performance can be improved by using a combination of character-based and parts-of-Arabic-words (PAW)-based SSMs
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
Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks
Millions of people around the world are diagnosed with neurological disorders like Parkinson’s, Cerebral Palsy or Amyotrophic Lateral Sclerosis. Due to the neurological damage as the disease progresses, the person suffering from the disease loses control of muscles, along with speech deterioration. Speech deterioration is due to neuro motor condition that limits manipulation of the articulators of the vocal tract, the condition collectively called as dysarthria. Even though dysarthric speech is grammatically and syntactically correct, it is difficult for humans to understand and for Automatic Speech Recognition (ASR) systems to decipher. With the emergence of deep learning, speech recognition systems have improved a lot compared to traditional speech recognition systems, which use sophisticated preprocessing techniques to extract speech features.
In this digital era there are still many documents that are handwritten many of which need to be digitized. Offline handwriting recognition involves recognizing handwritten characters from images of handwritten text (i.e. scanned documents). This is an interesting task as it involves sequence learning with computer vision. The task is more difficult than Optical Character Recognition (OCR), because handwritten letters can be written in virtually infinite different styles. This thesis proposes exploiting deep learning techniques like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for offline handwriting recognition. For speech recognition, we compare traditional methods for speech recognition with recent deep learning methods. Also, we apply speaker adaptation methods both at feature level and at parameter level to improve recognition of dysarthric speech
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