Speech Recognition using Linear Predictive Coding (LPC) and Adaptive Neuro-Fuzzy (ANFIS) to Control 5 DoF Arm Robot

Sanjaya, W. S. Mada; Anggraeni, Dyah; Santika, Ikhsan Purnama

doi:10.1088/1742-6596/1090/1/012046

Cited by 20 publications

(10 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This coefficient has been used for classifying cough sounds [49], [50], with suitable results. After extracting the LPC [37], [51], its first and second derivatives are calculated. Subsequently, all the computed features are fused to obtain a single LPC feature.…”

Section: ) Linear Predictive Code Coefficientmentioning

confidence: 99%

Classification of Cough Sounds Using Spectrogram Methods and a Parallel-Stream One-Dimensional Deep Convolutional Neural Network

Huang

Mushi

2022

IEEE Access

View full text Add to dashboard Cite

Currently, a subjective method is used to diagnose cough sounds, particularly wet and dry coughs, which can lead to incorrect diagnoses. In this study, novel emergent features were extracted using spectrogram methods and a parallel-stream one-dimensional (1D) deep convolutional neural network (DCNN) to classify cough sounds. The data of this study were obtained from two datasets. We employed the Mel spectrogram, chromagram constant-Q transform, Mel-frequency cepstral coefficient, constant-Q cepstral coefficient, and linear predictive code coefficient to conduct features analysis. The maximum, mean, variance, and standard deviation values of the original spectrogram as well as the maximum first and second derivatives of this spectrogram were extracted and fused to create a single-feature vector. We adopted two types of features: single features and combined features. Each design was restructured according to the magnitude of features with high discrimination power. A parallel-stream 1D-DCNN was developed for classifying cough sounds accurately. We compared the results obtained using the aforementioned network with those obtained using a single-stream 1D-DCNN. We found that the parallel-stream network outperformed the single-stream network for some feature sets. The developed network achieved F1 scores of 98.61% and 82.96% for the first and second datasets, respectively. The concatenation of layers at the flattening level resulted in an F1 score of 99.30% in dataset one. Moreover, layer merging strategies exhibited a better performance at the second convolutional layer level than at the flattening layer level in many cases.

show abstract

Section: ) Linear Predictive Code Coefficientmentioning

confidence: 99%

Classification of Cough Sounds Using Spectrogram Methods and a Parallel-Stream One-Dimensional Deep Convolutional Neural Network

Huang

Mushi

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To ensure stationary between frames, a standard overlap is placed between two contiguous frames. There is no signal lost for this reason [11,12]. Each audio sample is segmented into 25 ms frame with 50% overlap.…”

Section: Framingmentioning

confidence: 99%

A Novel Approach of Audio-Visual Color Recognition Using KNN

Paul

Dey

Adhikary

et al. 2021

Computational Intelligence in Pattern Recognition

View full text Add to dashboard Cite

Speech is one of the attractive areas of the scientists to research in the field of machine learning and they got maximum success in Automatic Speech Recognition system. ASR system gradually enters its footsteps into space exploration to home automation, education sectors to commercial sector, and various public sectors in our daily life to make it more manageable and comfortable. In our proposed work, we aimed to build a model on isolated Bengali word recognition system based on different colors pronounced in Bengali dialects that provides an audio-visual presentation of the recognized color. In this research work, LPC is used for extracting speech features based on pitch and fundamental frequency and KNN classifier for recognition. The proposed system achieved 94% testing accuracy for the dataset of 1500 audio samples for 15 classes where each class represents a specific color pronounced in Bengali dialect.

show abstract

“…Because the speech signal has many time-dependent variations, the estimate will cut a signal called a frame. The procedure for obtaining the LPC coefficient is shown in Figure 2 [14].…”

Section: Linear Predictive Coefficients (Lpcs)mentioning

confidence: 99%

Age Estimation in Short Speech Utterances Based on Bidirectional Gated-Recurrent Neural Networks

Badr¹,

Abdul-Hassan²

2021

ETJ

View full text Add to dashboard Cite

Recently, age estimates from speech have received growing interest as they are important for ‎many applications like custom call routing, targeted marketing, or user-profiling. In this work, an ‎automatic system to estimate age in short speech utterances without ‎depending on the text is proposed. From each utterance frame, four ‎groups of features are extracted and then 10 statistical functionals are measured for each ‎extracted dimension of the features, to be followed by dimensionality reduction using Linear ‎Discriminant Analysis (LDA). Finally, bidirectional Gated-Recurrent Neural Networks (G-‎RNNs) are used to predict speaker age. Experiments are conducted on the VoxCeleb1 ‎dataset to show the performance of the proposed system, which is the first attempt to do so for ‎such a system. In gender-dependent system, the Mean Absolute Error (MAE) of the proposed system ‎is 9.25 years, and 10.33 ‎years, the Root Mean ‎Square Error (RMSE)‎ is 13.17 and 13.26, respectively, ‎for ‎female and male speakers. In gender_ independent system, the MAE of the proposed system is 10.96 years, and the RMSE is 15.47. The results show that the proposed system has a good performance on short-duration utterances, taking into consideration the high noise ratio in the VoxCeleb1 dataset. ‎

show abstract

Speech Recognition using Linear Predictive Coding (LPC) and Adaptive Neuro-Fuzzy (ANFIS) to Control 5 DoF Arm Robot

Cited by 20 publications

References 3 publications

Classification of Cough Sounds Using Spectrogram Methods and a Parallel-Stream One-Dimensional Deep Convolutional Neural Network

Classification of Cough Sounds Using Spectrogram Methods and a Parallel-Stream One-Dimensional Deep Convolutional Neural Network

A Novel Approach of Audio-Visual Color Recognition Using KNN

Age Estimation in Short Speech Utterances Based on Bidirectional Gated-Recurrent Neural Networks

Contact Info

Product

Resources

About