Neuro-heuristic voice recognition

Połap, Dawid

doi:10.15439/2016f128

Cited by 5 publications

(2 citation statements)

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“…Quantization is the process of preparing a signal in digital domain and making it suitable for processing by a computer or any digital circuit [9]. Considering the growing interest in man-machine communication, speech and voice recognition is considered as important [37], [23][24], [36], [7], [16], [25]. Recent research and applications, which exploit neural networks, commonly incorporate quantization of weight coefficients and activation functions.…”

Section: Introductionmentioning

confidence: 99%

Simple Speech Transform Coding Scheme using Forward Adaptive Quantization for Discrete Input Signal

Perić

Tančić

Simic

et al. 2019

ITC

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We propose a speech coding scheme based on the simple transform coding and forward adaptive quantization for discrete input signal processing in this paper. The quasi-logarithmic quantizer is applied to discretization of continuous input signal, i.e. for preparing discrete input. The application of forward adaptation based on the input signal variance provides more efficient bandwidth usage, whereas utilization of transform coding provides sub-sequences with more predictable signal characteristics that ensure higher quality of signal reconstruction at the receiving end. In order to provide additional compression, transform coding precedes adaptive quantization. As an objective measure of system performance we use signal-to-quantization-noise ratio. Sysem performance is discussed for two typical cases. In the first case, we consider that the information about continuous signal variance is available whereas the second case considers system performance estimation when we know only the information about discretized signal variance which means that there is a loss of input signal information. The main goal of such performance estimation comparison of the proposed speech signal coding model is to explore what is the objectivity of performance if we do not have information about a continuous source, which is a common phenomenon in digital systems.

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Section: Introductionmentioning

confidence: 99%

Simple Speech Transform Coding Scheme using Forward Adaptive Quantization for Discrete Input Signal

Perić

Tančić

Simic

et al. 2019

ITC

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“…Sound analysis is very often associated with the processing of sound files or even images. In [1], the idea of using heuristic algorithms in conjunction with the neural classifier has been introduced as a tool for identity verification process. Again in [2], the authors presented the use of modified mellin transform for detection of selected voice disorders.…”

Section: Introductionmentioning

confidence: 99%

Extraction of specific data from a sound sample by removing additional distortion

Połap

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-Correct identity recognition based on a voice sample must deal with many problems such as too big or small distance from the microphone, noise or abnormal voice. Hoarseness, coughing or even stuttering can also be encountered as disturbance of the voice. Research on new aspects of intelligent processing for voice brings possibilities to use intelligent methods to increase efficiency in processing and quality of record. In this paper, a spectrogram analysis for the detection of specific data and remove these distortions in the sample is presented. The proposed solution has been tested and discussed for real use in identity verification systems.

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State Flipping Based Hyper-Heuristic for Hybridization of Nature Inspired Algorithms

Damaševičius

Woźniak

2017

Artificial Intelligence and Soft Computing

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Neuro-heuristic voice recognition

Cited by 5 publications

References 20 publications

Simple Speech Transform Coding Scheme using Forward Adaptive Quantization for Discrete Input Signal

Simple Speech Transform Coding Scheme using Forward Adaptive Quantization for Discrete Input Signal

Extraction of specific data from a sound sample by removing additional distortion

State Flipping Based Hyper-Heuristic for Hybridization of Nature Inspired Algorithms

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