Fast speaker adaptation of artificial neural networks for automatic speech recognition

Methods based on artificial neural networks (ANN) are widely used in various audio signal processing tasks. This provides opportunities to optimize processes and save resources required for calculations. One of the main objects we need to get to numerically capture the acoustics of a room is the room impulse response (RIR). Increasingly, research authors choose not to record these impulses in a real room but to generate them using ANN, as this gives them the freedom to prepare unlimited-sized training datasets. Neural networks are also used to augment the generated impulses to make them similar to the ones actually recorded. The widest use of ANN so far is observed in the evaluation of the generated results, for example, in automatic speech recognition (ASR) tasks. This review also describes datasets of recorded RIR impulses commonly found in various studies that are used as training data for neural networks.

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An efficient noise-robust automatic speech recognition system using artificial neural networks

2016 International Conference on Communication and Signal Processing (ICCSP) ◽

10.1109/iccsp.2016.7754495 ◽

2016 ◽

Cited By ~ 2

Author(s):

Santosh Gupta ◽

Kishor M. Bhurchandi ◽

Avinash G. Keskar

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Speech Recognition ◽

Automatic Speech Recognition ◽

Recognition System ◽

Speech Recognition System ◽

Automatic Speech Recognition System ◽

Artificial Neural ◽

Noise Robust

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Automatic speech recognition using hidden Markov models and artificial neural networks

IEEE International Conference on Neural Networks ◽

10.1109/icnn.1993.298825 ◽

2002 ◽

Author(s):

N.M. Botros ◽

M. Siddiqi ◽

M.Z. Deiri

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Speech Recognition ◽

Hidden Markov Models ◽

Automatic Speech Recognition ◽

Markov Models ◽

Hidden Markov ◽

Artificial Neural

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Real-time frequency-based noise-robust Automatic Speech Recognition using Multi-Nets Artificial Neural Networks: A multi-views multi-learners approach

Neurocomputing ◽

10.1016/j.neucom.2013.09.040 ◽

2014 ◽

Vol 129 ◽

pp. 199-207 ◽

Cited By ~ 25

Author(s):

Seyed Reza Shahamiri ◽

Siti Salwah Binti Salim

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Speech Recognition ◽

Real Time ◽

Automatic Speech Recognition ◽

Time Frequency ◽

Artificial Neural ◽

Noise Robust

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Speech Assistance for Persons With Speech Impediments Using Artificial Neural Networks

Volume 3: Biomedical and Biotechnology Engineering ◽

10.1115/imece2017-71027 ◽

2017 ◽

Author(s):

Ramy Mounir ◽

Redwan Alqasemi ◽

Rajiv Dubey

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Speech Recognition ◽

Automatic Speech Recognition ◽

Challenging Problem ◽

Speech Impairment ◽

Recognition Model ◽

Wide Range ◽

Speech Variability

This work focuses on the research related to enabling individuals with speech impairment to use speech-to-text software to recognize and dictate their speech. Automatic Speech Recognition (ASR) tends to be a challenging problem for researchers because of the wide range of speech variability. Some of the variabilities include different accents, pronunciations, speeds, volumes, etc. It is very difficult to train an end-to-end speech recognition model on data with speech impediment due to the lack of large enough datasets, and the difficulty of generalizing a speech disorder pattern on all users with speech impediments. This work highlights the different techniques used in deep learning to achieve ASR and how it can be modified to recognize and dictate speech from individuals with speech impediments.

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