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.

In this work, we focus on estimating articulatory movements from acoustic features, known as acoustic-to-articulatory inversion (AAI), for dysarthric patients with amyotrophic lateral sclerosis (ALS). Unlike healthy subjects, there are two potential challenges involved in AAI on dysarthric speech. Due to speech impairment, the pronunciation of dysarthric patients is unclear and inaccurate, which could impact the AAI performance. In addition, acoustic-articulatory data from dysarthric patients is limited due to the difficulty in the recording. These challenges motivate us to utilize cross-corpus acoustic-articulatory data. In this study, we propose an AAI model by conditioning speaker information using x-vectors at the input, and multi-target articulatory trajectory outputs for each corpus separately. Results reveal that the proposed AAI model shows relative improvements of the Pearson correlation coefficient (CC) by ~13.16% and ~16.45% over a randomly initialized baseline AAI model trained with only dysarthric corpus in seen and unseen conditions, respectively. In the seen conditions, the proposed AAI model outperforms the three baseline AAI models, that utilize the cross-corpus, by ~3.49%, ~6.46%, and ~4.03% in terms of CC.

In this paper, we consider 2-class and 3-class classification problems for classifying patients with Amyotrophic Lateral Sclerosis (ALS), Parkinson's Disease (PD), and Healthy Controls (HC) using a CNNLSTM network. Classification performance is examined for three different tasks, namely, Spontaneous speech (SPON), Diadochokinetic rate (DIDK) and Sustained phoneme production (PHON). Experiments are conducted using speech data recorded from 60 ALS, 60 PD, and 60 HC subjects. Classifications using SVM and DNN are considered as baseline schemes. Classification accuracy of ALS and HC (indicated by ALS/HC) using CNN-LSTM has shown an improvement of 10.40%, 4.22% and 0.08% for PHON, SPON and DIDK tasks, respectively over the best of the baseline schemes. Furthermore, the CNN-LSTM network achieves the highest PD/HC classification accuracy of 88.5% for the SPON task and the highest 3-class (ALS/PD/HC) classification accuracy of 85.24% for the DIDK task. Experiments using transfer learning at low resource training data show that data from ALS benefits PD/HC classification and vice-versa. Experiments with fine-tuning weights of 3-class (ALS/PD/HC) classifier for 2-class classification (PD/HC or ALS/HC) gives an absolute improvement of 2% classification accuracy in SPON task when compared with randomly initialized 2-class classifier.

In this work, we consider the task of acoustic and articulatory feature based automatic classification of Amyotrophic Lateral Sclerosis (ALS) patients and healthy subjects using speech tasks. In particular, we compare the roles of different types of speech tasks, namely rehearsed speech, spontaneous speech and repeated words for this purpose. Simultaneous articulatory and speech data were recorded from 8 healthy controls and 8 ALS patients using AG501 for the classification experiments. In addition to typical acoustic and articulatory features, new articulatory features are proposed for classification. As classifiers, both Deep Neural Networks (DNN) and Support Vector Machines (SVM) are examined. Classification experiments reveal that the proposed articulatory features outperform other acoustic and articulatory features using both DNN and SVM classifier. However, SVM performs better than DNN classifier using the proposed feature. Among three different speech tasks considered, the rehearsed speech was found to provide the highest F-score of 1, followed by an F-score of 0.92 when both repeated words and spontaneous speech are used for classification.

In the recordings using electromagnetic articulograph AG 501, sensors are glued to subject’s articulators such as jaw, lips and tongue and both speech and articulatory movements are simultaneously recorded. In this work, we study the effect of the presence of the sensors on the quality of speech spoken by the subject. This is done by recording when a subject speaks a set of 19 VCV stimuli while sensors are attached to subject’s articulators. For comparison we also record the same set of stimuli spoken by the same subject but with no sensors attached to subject’s articulators. Both subjective and objective comparisons are made on the recorded stimuli in these two settings. Subjective evaluation is carried out using 16 evaluators. Listening experiments with recordings from five subjects show that the recordings with sensors attached are significantly different from those without sensors attached in terms of human recognition score as well as on a perceptual difference measure. This is also supported in the objective comparison which computes dissimilarity measure using the spectral shape information. Index Terms: Electromagnetic Articulography, speech quality, listening test