• Corpus ID: 61058350

Connectionist Speech Recognition: A Hybrid Approach

@inproceedings{Bourlard1993ConnectionistSR,
  title={Connectionist Speech Recognition: A Hybrid Approach},
  author={Herv{\'e} Bourlard and Nelson Morgan},
  year={1993}
}
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 speech recognition systems based on Hidden Markov Models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e., HMM emission probability estimation and… 

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References

SHOWING 1-10 OF 196 REFERENCES

Statistical Inference in Multilayer Perceptrons and Hidden Markov Models with Applications in Continuous Speech Recognition

TLDR
The statistical use of a particular classic form of a connectionist system, the Multilayer Perceptron (MLP), is described in the context of the recognition of continuous speech.

Continuous Speech Recognition Using Segmental Neural Nets

TLDR
A hybrid SNN/HMM system has been developed to combine the advantages of both types of approaches and use is made of the N-best paradigm to generate likely phonetic segmentations, which are then scored by the SNN.

A Continuous Speech Recognition System Embedding MLP into HMM

TLDR
It is shown here that word recognition performance for a simple discrete density HMM system appears to be somewhat better when MLP methods are used to estimate the emission probabilities.

Links Between Markov Models and Multilayer Perceptrons

TLDR
It is shown theoretically and experimentally that the outputs of the MLP approximate the probability distribution over output classes conditioned on the input, i.e. the maximum a posteriori probabilities.

The HARPY speech recognition system

TLDR
The HARPY system is the result of an attempt to understand the relative importance of various design choices of two earlier speech recognition systems developed at Carnegie-Mellon University, in which knowledge is represented as a finite state transition network but without the a-priori transition probabilities.

Connectionist probability estimation in the DECIPHER speech recognition system

TLDR
Results indicate that connectionist probability estimation can improve performance of a context-independent maximum-likelihood-trained HMM system; performance of the connectionist system is close to what can be achieved using (context-dependent) HMM systems of much higher complexity.

Hybrid neural network/hidden Markov model continuous-speech recognition

TLDR
A hybrid multilayer perceptron (MLP)/hidde arkov model (HMM) speaker-independent continuous-speech recogni-b tion system, in which the advantages of both approaches are combined using MLPs to estimate the state-dependent observation probabilities of an HMM.

Phoneme recognition using time-delay neural networks

The authors present a time-delay neural network (TDNN) approach to phoneme recognition which is characterized by two important properties: (1) using a three-layer arrangement of simple computing

Review of Neural Networks for Speech Recognition

TLDR
Further work is necessary for large-vocabulary continuous-speech problems, to develop training algorithms that progressively build internal word models, and to develop compact VLSI neural net hardware.

Phoneme recognition: neural networks vs. hidden Markov models vs. hidden Markov models

TLDR
A time-delay neural network for phoneme recognition that was able to invent without human interference meaningful linguistic abstractions in time and frequency such as formant tracking and segmentation and does not rely on precise alignment or segmentation of the input.
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