Couchbase Capella is a scalable document-oriented database service in the cloud. Its existing Capella Operational service is based on a shared-nothing architecture and supports high volumes of low-latency queries and updates for JSON documents. Its new Capella Columnar cloud service complements the Operational service. The Capella Columnar service supports complex analytical queries (e.g., ad hoc joins and aggregations) over large collections of JSON documents that can originate from a variety of Couchbase and non-Couchbase data sources and formats and can either be stored and managed by the Capella Columnar service or externally stored and accessed on demand at query time. This paper describes the new Capella Columnar service, looking both over and under the hood.

Individuals with dysarthria are unable to control rapid movement of the velum leading to reduction in intelligibility, audibility, naturalness and efficiency of vocal communication. Automatic intelligibility assessment of dysarthric patients allows clinicians diagnose the impact of therapy and medication and also to plan future course of action. Earlier works have concentrated on building speaker dependent machine learning systems for intelligibility assessment, due to limited availability of data. However, a speaker independent assessment system is of greater use by clinicians. Motivated by this observation, we propose a speaker independent intelligibility assessment system which relies on a novel set of features obtained by processing the output of DeepSpeech, an end to end Speech-to-Text engine. All experiments have been performed on the Universal Access Speech database. An accuracy of 53.9% was obtained using Support Vector Machine based four-class classification system for the speaker independent scenario while the accuracy obtained for the speaker dependent scenario is 97.4%.

Dysarthria is a motor speech impairment caused by muscle weakness. Individuals, with this condition, are unable to control rapid movement of the velum leading to reduction in intelligibility, audibility, naturalness and efficiency of vocal communication. Systems that can assess intelligibility of dysarthric speech can help clinicians diagnose the impact of therapy and medication. In the paper, we propose a usable novel method to assess intelligibility of dysarthric speakers. The approach is based on the observation that the performance of a speech recognition engine deteriorates with increase in severity of the disorder. The mismatch between the original word and the recognized string is exploited to compute the dysarthria intelligibility score. Experiments on UA speech corpus show that the computed intelligibility score exhibits a significant correlation with perceptually assessed intelligibility scores. We further show that a small set of words spoken by the dysarthric subject is sufficient to assess the speech intelligibility reliably.

Imbalance in the proportion of training samples belonging to different classes often poses performance degradation of conventional classifiers. This is primarily due to the tendency of the classifier to be biased towards the majority classes in the imbalanced dataset. In this paper, we propose a novel three step technique to address imbalanced data. As a first step we significantly oversample the minority class distribution by employing the traditional Synthetic Minority Oversampling Technique (SMOTE) algorithm using the neighborhood of the minority class samples and in the next step we partition the generated samples using a Gaussian-Mixture Model based clustering algorithm. In the final step synthetic data samples are chosen based on the weight associated with the cluster, the weight itself being determined by the distribution of the majority class samples. Extensive experiments on several standard datasets from diverse domains show the usefulness of the proposed technique in comparison with the original SMOTE and its state-of-the-art variants algorithms.

Hypernasality is a common characteristic symptom across many motor-speech disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies and, for unvoiced sounds, there is reduced articulatory precision due to air escaping through the nasal cavity. However, the acoustic manifestation of these symptoms is highly variable, making hypernasality estimation very challenging, both for human specialists and automated systems. Previous work in this area relies on either engineered features based on statistical signal processing or machine learning models trained on clinical ratings. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, whereas metrics based on machine learning are prone to overfitting to the small disease-specific speech datasets on which they are trained. Here we propose a new set of acoustic features that capture these complementary dimensions. The features are based on two acoustic models trained on a large corpus of healthy speech. The first acoustic model aims to measure nasal resonance from voiced sounds, whereas the second acoustic model aims to measure articulatory imprecision from unvoiced sounds. To demonstrate that the features derived from these acoustic models are specific to hypernasal speech, we evaluate them across different dysarthria corpora. Our results show that the features generalize even when training on hypernasal speech from one disease and evaluating on hypernasal speech from another disease (e.g., training on Parkinson's disease, evaluation on Huntington's disease), and when training on neurologically disordered speech but evaluating on cleft palate speech.