• Corpus ID: 28558623

Inverted HMM - a Proof of Concept

@inproceedings{Doetsch2016InvertedH,
  title={Inverted HMM - a Proof of Concept},
  author={Patrick Doetsch and Hermann Ney and Stefan Hegselmann and Ralf Schl{\"u}ter},
  booktitle={NIPS 2016},
  year={2016}
}
In this work, we propose an inverted hidden Markov model (HMM) approach to automatic speech and handwriting recognition that naturally incorporates discriminative, artificial neural network based label distributions. [] Key Method The approach does not assume the usual decomposition into a separate (generative) acoustic model and a language model, and allows for a variety of model assumptions, incl. statistical variants of attention.
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References

SHOWING 1-10 OF 21 REFERENCES
End-to-end attention-based large vocabulary speech recognition
TLDR
This work investigates an alternative method for sequence modelling based on an attention mechanism that allows a Recurrent Neural Network (RNN) to learn alignments between sequences of input frames and output labels.
GMM-Free Flat Start Sequence-Discriminative DNN Training
TLDR
The sequence-discriminative flat start training method is not only significantly faster than the straightforward approach of iterative retraining and realignment, but the word error rates attained are slightly better as well.
Tandem connectionist feature extraction for conventional HMM systems
TLDR
A large improvement in word recognition performance is shown by combining neural-net discriminative feature processing with Gaussian-mixture distribution modeling.
Sequence to Sequence Learning with Neural Networks
TLDR
This paper presents a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure, and finds that reversing the order of the words in all source sentences improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.
Connectionist Speech Recognition: A Hybrid Approach
From the Publisher: Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous…
Listen, attend and spell: A neural network for large vocabulary conversational speech recognition
We present Listen, Attend and Spell (LAS), a neural speech recognizer that transcribes speech utterances directly to characters without pronunciation models, HMMs or other components of traditional…
End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results
TLDR
Initial results demonstrate that this new approach achieves phoneme error rates that are comparable to the state-of-the-art HMM-based decoders, on the TIMIT dataset.
Semi-supervised maximum mutual information training of deep neural network acoustic models
TLDR
It is shown that if the supervision transcripts are not known, the natural analogue of MMI is to minimize the conditional entropy of the lattice of possible transcripts of the data, equivalent to the weighted average of M MI criterion over different reference transcripts, taking those reference transcripts and their weighting from the lattICE itself.
Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI
TLDR
A method to perform sequencediscriminative training of neural network acoustic models without the need for frame-level cross-entropy pre-training is described, using the lattice-free version of the maximum mutual information (MMI) criterion: LF-MMI.
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
TLDR
This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems of sequence learning and post-processing.
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