Adaptive optimization based neural network for classification of stuttered speech

Manjula, G.; Shivakumar, M.; Geetha, Y. V.

doi:10.1145/3309074.3309113

Cited by 15 publications

(20 citation statements)

References 8 publications

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“…The network is trained on 68 Latin American Spanish speakers. The work in [18] used adaptive optimization based neural network for three class stuttering classification.…”

Section: Related Workmentioning

confidence: 99%

StutterNet: Stuttering Detection Using Time Delay Neural Network

Sheikh¹,

Sahidullah²,

Hirsch³

et al. 2021

Preprint

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This paper introduce StutterNet, a novel deep learning based stuttering detection capable of detecting and identifying various types of disfluencies. Most of the existing work in this domain uses automatic speech recognition (ASR) combined with language models for stuttering detection. Compared to the existing work, which depends on the ASR module, our method relies solely on the acoustic signal. We use a time-delay neural network (TDNN) suitable for capturing contextual aspects of the disfluent utterances. We evaluate our system on the UCLASS stuttering dataset consisting of more than 100 speakers. Our method achieves promising results and outperforms the state-ofthe-art residual neural network based method. The number of trainable parameters of the proposed method is also substantially less due to the parameter sharing scheme of TDNN.

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“…The network is trained on 68 Latin American Spanish speakers. The work in [18] used adaptive optimization based neural network for three class stuttering classification.…”

Section: Related Workmentioning

confidence: 99%

StutterNet: Stuttering Detection Using Time Delay Neural Network

Sheikh¹,

Sahidullah²,

Hirsch³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Pathological speech, especially stuttering, is characterised by variations in breathing, phonation, speech speed, speech rates, rhythm and pronunciation [ 3 , 11 ], which procure it to be much more complex. Moreover, people with speech impediments generate signals whose application in pattern recognition systems is not as effective as for clear speech [ 12 ] because a lot of information is unclear, mixed and hidden.…”

Section: Introductionmentioning

confidence: 99%

“…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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“…The optimization algorithm is a multi-agent system and addresses the problem of identifying the typical solutions in polynomial time. [25], [37], [44]. The metaheuristic algorithm general process is represented in Fig.…”

Section: Introductionmentioning

confidence: 99%

Survey on optimization algorithms in speech processing

Manjutha

Subashini

2022

ijhs

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Nature is a tremendous provenance of resolving hard and complex problems that exist in the field of computer science because it reveals a very diverse, robust, dynamic, and interesting phenomenon. It constantly finds the optimal solution to resolve its problem which accomplishes exact equity among its element. Nature-inspired algorithms are the heuristic high-level procedure that interprets nature to solve the optimization problem which popularized in the new era of computation. The main objective of this paper is to evaluate the modern technology and enhancement in the nature-inspired algorithm, especially in the application of speech processing, speech recognition, and speech feature selection problems. This paper presents broad collections of global optimization algorithms which have been successfully applied to generate recognition systems that are integrated with metaheuristic algorithms.

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Adaptive optimization based neural network for classification of stuttered speech

Cited by 15 publications

References 8 publications

StutterNet: Stuttering Detection Using Time Delay Neural Network

StutterNet: Stuttering Detection Using Time Delay Neural Network

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

Survey on optimization algorithms in speech processing

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