Dysarthria due to Amyotrophic Lateral Sclerosis (ALS) affects the acoustic characteristics of different speech sounds. The effects intensify with increasing severity leading to the collapse of the acoustic space of the affected individuals. With an aim to characterize such changes in the acoustic space, this paper studies the variations in band-specific and full-band spectral properties of 4 sustained vowels (/a/, /i/, /o/, /u/) and 3 sustained fricatives (/s/, /sh/, /f/) at different dysarthria severity levels. Effect of dysarthria on spectral features of these phonemes are not well explored. Statistical comparison of these features among different severities for the phonemes considered and among different vowels/fricatives for every severity level using speech data from 119 ALS and 40 healthy subjects indicate the followings. Though all band-specific and full-band features of the three fricatives and most of those features for the four vowels become statistically similar at high severity levels, certain features remain distinguishable. Spectral differences in 0-2 kHz band between /a/ and the other vowels and in the 2-6 kHz band between /a/ and /o/, /u/ persist through all severity levels. Moreover, properties of /f/ remain mostly unchanged with increasing dysarthria severity levels.

Dysarthria due to Amyotrophic Lateral Sclerosis (ALS) and Parkinson’s Disease (PD) impairs sustained phoneme productions. Vowels and fricatives get affected differently owing to the differences in their production mechanisms. This paper examines three sustained voiceless fricatives - /s/, /sh/ and /f/, as compared to three sustained vowels - /a/, /i/ and /o/, for classifying patients with ALS/PD and Healthy Controls (HC). Fricatives are found to achieve higher classification accuracies than /a/ and /o/, though /i/ outperforms all. Patients seem to find it difficult to form constrictions while producing fricatives, or to proximally position the tongue and palate while uttering /i/, due to dysarthria. Unwanted voicing added to voiceless fricatives by the patient population further contributes towards the discrimination. Both source (related to vocal cord) and filter (related to vocal tract) cues of fricatives, on average, outperform those of vowels. Lastly, decision-level fusion of /i/-/s/-/sh/, with a pooled classifier for these three phonemes, achieves the highest mean ALS vs. HC classification accuracy of 83.35%, although in PD vs. HC case, fusion of multiple /i/ utterances performs the best with an accuracy of 80.03%.

Dysarthria due to Amyotrophic Lateral Sclerosis (ALS) affects speech production. Even the elementary sustained vowel utterances get impaired. For these, the impairments can be in achieving vowel-specific articulatory configurations, reflected in static acoustic cues, and/or in sustaining a configuration for a prolonged duration, reflected in dynamic cues. Such cues can further be attributed to the vocal cord (source) and vocal tract (filter) involved in speech production. This paper analyzes the relative contributions of these static (captured through average spectral characteristics) and dynamic (captured through spectral variations over time) source and filter cues toward automatic classification of ALS patients and healthy subjects using sustained utterances of /a/, /i/, /o/ and /u/. Experiments with 80 ALS patients and 80 healthy subjects suggest that the source cues (static/dynamic) are not the primary discriminators. For /i/, the static filter cues achieve the highest mean classification accuracy of 76.66%, whereas, for /a/, /o/ and /u/, the dynamic filter attributes contribute the most attaining average accuracies of 66.29%, 73.03% and 70.27%, respectively. Hence, ALS patients seem to face difficulties in forming the front closed vocal tract structure of /i/, whereas, holding the target vocal tract shape for long appears to be the primary challenge in case of /a/, /o/ and /u/.

Dysarthria due to Amyotrophic Lateral Sclerosis (ALS) and Parkinson’s disease (PD) impacts both articulation and prosody in an individual’s speech. Complex deep neural networks exploit these cues for detection of ALS and PD. These are typically done using recordings in laboratory condition. This study aims to examine the robustness of these cues against background noise and model complexity, which has not been investigated before. We perform classification experiments with pitch and Mel-frequency cepstral coefficients (MFCC) using models of three different complexities and additive white Gaussian noise in four signal-to-noise-ratio (SNR) conditions. The findings are as follows: 1) In clean condition, pitch performs similar to MFCC across most model complexities considered, suggesting that one-dimensional pitch pattern provides discriminative cues for the classification to an extent equal to that of multi-dimensional MFCC, 2) Similar trend is observed in noisy cases when classifiers are trained and tested in matched noise and SNR conditions, 3) When the classifiers trained on clean data are applied in noisy cases, pitch based average classification accuracies are found to be 20.09% and 24.73% higher than those using MFCC for ALS vs. healthy and PD vs. healthy, respectively, suggesting robustness of pitch based classifier against noise and model complexity.

P300 is one of the most widely studied event-related potentials. Unfortunately, most of the existing automatic P300 detection schemes require computations over repetitive trials in both training and recognition phases. Several attempts have recently been endeavored towards the single trial detection of the P300 signals. However, no acceptable solution to the problem is found till date. In the present work, we have attempted to address this problem in the light of latency and (amplitude) deflection of the signal. The intra- and inter-personal variations inherent in these features are managed by the uncertainty management characteristics of General Type-2 Fuzzy Sets. First, these sets are constructed by exploiting the knowledge obtained from different trials of a large number of subjects. The secondary membership functions of the Type-2 Fuzzy Sets are computed based on a novel density dependent measure of the primary membership functions in the footprint of uncertainty. Second, recognition of P300 in an unknown EEG trial is performed based on the agreement of measured feature values with the General Type-2 Fuzzy knowledge-base. Majority voting of the concerned electrodes makes the scheme more robust. The experimental results show that the proposed algorithm is capable of achieving 88.60% accuracy in single trial detection of P300 instances, which is significantly higher than those obtained in state of the art algorithms.