Mongolian Speech Recognition Based on Deep Neural Networks

@inproceedings{Zhang2015MongolianSR,
  title={Mongolian Speech Recognition Based on Deep Neural Networks},
  author={Hui Zhang and Feilong Bao and Guanglai Gao},
  booktitle={CCL},
  year={2015}
}
Mongolian is an influential language. And better Mongolian Large Vocabulary Continuous Speech Recognition (LVCSR) systems are required. Recently, the research of speech recognition has achieved a big improvement by introducing the Deep Neural Networks (DNNs). In this study, a DNN-based Mongolian LVCSR system is built. Experimental results show that the DNN-based models outperform the conventional models which based on Gaussian Mixture Models (GMMs) for the Mongolian speech recognition, by a… 

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References

SHOWING 1-10 OF 24 REFERENCES

A Mongolian Speech Recognition System Based on HMM

TLDR
A Mongolian large vocabulary continuous speech recognition system is introduced and the experimental results indicated that the design of models related to the Mongolian speech recognition is rational and correct.

Improving of Acoustic Model for the Mongolian Speech Recognition System

TLDR
The basic resources of Mongolian speech recognition system are optimized, and system recognition accuracy rates of word and sentence have been greatly improved and system performance has been optimized.

Researching of Speech Recognition Oriented Mongolian Acoustic Model

TLDR
M Mongolian context-dependent acoustic model based on decision tree was proposed and decision tree based state tying was applied to the acoustic model designning in Mongolian speech recognition.

Segmentation-based Mongolian LVCSR approach

TLDR
A segmentation-based Mongolian Large Vocabulary Continuous Speech Recognition (LVCSR) approach is proposed and results show that, by converting most of these words into their corresponding In-Vocabulary form, the proposed approach effectively recognizes most of the Mongolian words and greatly improves the sample sparseness problem in the language model.

A design and implementation of HMM based Mongolian speech recognition system

TLDR
The design and development of HMM-based speech recognition system for the Mongolian language based on Hidden Markov Models is described and the performance of isolated word recognition with context independent and context dependent models is evaluated.

Recurrent neural network based language model

TLDR
Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model.

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

TLDR
This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.

Deep Recurrent Neural Networks for Acoustic Modelling

TLDR
A novel deep Recurrent Neural Network model for acoustic modelling in Automatic Speech Recognition (ASR) that combines a Deep Neural Network with Time Convolution (TC), followed by a Bidirectional Long Short-Term Memory (BLSTM), and a final DNN.

Comparison of feedforward and recurrent neural network language models

TLDR
A simple and efficient method to normalize language model probabilities across different vocabularies is proposed, and it is shown how to speed up training of recurrent neural networks by parallelization.

ImageNet classification with deep convolutional neural networks

TLDR
A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.