Artificial Neural Networks in the Disabled Speech Analysis

A Lightly Supervised Approach to Detect Stuttering in Children's Speech

“…Early studies in disfluency detection that appeared to report positive results [15,16] lacked complete statistical findings; therefore, their significance cannot be determined. Other studies presented stuttering events in isolated speech segments to an artificial neural network (ANN), such that the ANN was actually performing a classification task, rather than recognition in continuous speech [17,18]. Further studies used a hybrid approach to detect stuttering in childrens reading tasks: Heeman et al [19,20] merged ASR outputs with the clinician's own manual annotations to produce corrected transcripts of the stuttering speech; this approach could not be described as fully automatic.…”

Comparison of speech parameterization techniques for the classification of speech disfluencies

“…The number of PLP and LPC coefficients depends on the order of the LPC. A different number of MFCCs (13,15,17, and 24) is used to characterize the disfluent speech. In this work, a 10-fold cross-validation scheme is used to prove the reliability of the classification results.…”

Machine Learning for Stuttering Identification: Review, Challenges and Future Directions

“…The various feature extraction methods that have been explored in the stuttering recognition systems are autocorrelation function and envelope parameters [78], duration, energy peaks, spectral of word based and part word based [79][80][81], age, sex, type of disfluency, frequency of disfluency, duration, physical concomitant, rate of speech, historical, attitudinal and behavioral scores, family history [38], duration and frequency of disfluent portions, speaking rate [26], frequency, 1 𝑠𝑡 to 3 𝑟𝑑 formant's frequencies and its amplitudes [81,82], spectral measure (fast Fourier transform (FFT) 512) [83,84], mel frequency cepstral coefficients (MFCC) [81,[85][86][87], Linear Predictive Cepstral Coefficients (LPCCs) [81,86], pitch, shimmer [88], zero crossing rate (ZCR) [81], short time average magnitude, spectral spread [81], linear predictive coefficients (LPC), weighted linear prediction cepstral coefficients (WLPCC) [86], maximum autocorrelation value (MACV) [81], linear prediction-Hilbert transform based MFCC (LH-MFCC) [89], noise to harmonic ratio, shimmer harmonic to noise ratio , harmonicity, amplitude perturbation quotient, formants and its variants (min, max, mean, median, mode, std), spectrum centroid [88], Kohonen's self-organizing Maps [84], i-vectors [90], perceptual linear predictive (PLP) [87], respiratory biosignals [39], and sample entropy feature [91]. With the recent developments in convolutional neural networks, the feature representation of stuttered speech is moving towards spectrogram representations from conventional MFCCs.…”