Neural Network based Speech Assistance tool to enhance the fluency of adults who stutter

Narasimhan, Sharan; Rao, Rohini R.

doi:10.1109/discover47552.2019.9008034

Cited by 1 publication

(2 citation statements)

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“…Generally, pattern recognition systems consist of two main components: feature analysis and pattern classification. Most state-of-the-art speech recognition systems are based on hidden Markov models (HMMs) or artificial neural networks (ANNs), or HMM and ANN hybrids [ 12 , 13 , 14 , 15 ]. Neural networks play an important role both in speech [ 15 , 16 , 17 ] and speaker recognition [ 18 , 19 , 20 , 21 ], mainly due to the development of new neural network topologies as well as training and classification algorithms [ 14 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…Neural networks play an important role both in speech [ 15 , 16 , 17 ] and speaker recognition [ 18 , 19 , 20 , 21 ], mainly due to the development of new neural network topologies as well as training and classification algorithms [ 14 , 22 , 23 ]. They have also been used for tasks such as classification [ 12 , 24 , 25 ] or feature extraction [ 26 , 27 ], isolated word recognition [ 28 ], small and large vocabulary and continuous speech recognition [ 29 , 30 ], as well as in disordered speech processing [ 7 , 8 , 12 , 13 , 31 , 32 , 33 , 34 , 35 , 36 ]. However, results achieved by recognition systems strongly depend on the input data.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Networks Combined with the Principal Component Analysis for Non-Fluent Speech Recognition

Świetlicka

Kuniszyk-Jóźkowiak

Świetlicki

2022

Sensors

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The presented paper introduces principal component analysis application for dimensionality reduction of variables describing speech signal and applicability of obtained results for the disturbed and fluent speech recognition process. A set of fluent speech signals and three speech disturbances—blocks before words starting with plosives, syllable repetitions, and sound-initial prolongations—was transformed using principal component analysis. The result was a model containing four principal components describing analysed utterances. Distances between standardised original variables and elements of the observation matrix in a new system of coordinates were calculated and then applied in the recognition process. As a classifying algorithm, the multilayer perceptron network was used. Achieved results were compared with outcomes from previous experiments where speech samples were parameterised with the Kohonen network application. The classifying network achieved overall accuracy at 76% (from 50% to 91%, depending on the dysfluency type).

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