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Multimodal depression classification has gained immense popularity over the recent years. We develop a multimodal depression classification system using articulatory coordination features extracted from vocal tract variables and text transcriptions obtained from an automatic speech recognition tool that yields improvements of area under the receiver operating characteristics curve compared to unimodal classifiers (7.5% and 13.7% for audio and text respectively).

The Speech Emotion Recognition Adaptation Benchmark (SERAB) is a new framework to evaluate the performance and generalization capacity of different approaches for utterance-level SER. The benchmark is composed of nine datasets for SER in six languages. We used the proposed framework to evaluate a selection of standard hand-crafted feature sets and state-of-the-art DNN representations. The results highlight that using only a subset of the data included in SERAB can result in biased evaluation, while compliance with the proposed protocol can circumvent this issue.

Depression is a frequent and curable psychiatric disorder, detrimentally affecting daily activities, harming both work-place productivity and personal relationships. Among many other symptoms, depression is associated with disordered
speech production, which might permit its automatic screening by means of the speech of the subject. However, the choice of actual features extracted from the recordings is not trivial. In this study, we employ x-vectors, a DNN-based

Hypernasality refers to the perception of abnormal nasal resonances in vowels and voiced consonants. Estimation of hypernasality severity from connected speech samples involves learning a mapping between the frame-level features and utterance-level clinical ratings of hypernasality. However, not all speech frames contribute equally to the perception of hypernasality.

Depression detection from speech continues to attract significant research attention but remains a major challenge, particularly when the speech is acquired from diverse smartphones in natural environments. Analysis methods based on vocal tract coordination have shown great promise in depression and cognitive impairment detection for quantifying relationships between features over time through eigenvalues of multi-scale cross-correlations.

In this paper, we consider 2-class and 3-class classification problems for classifying patients with Amyotropic Lateral Sclerosis (ALS), Parkinson’s Disease (PD) and Healthy Controls (HC) using a CNN-LSTM network. Classification performance is examined for three different tasks, namely, Spontaneous speech (SPON), Diadochoki-netic rate (DIDK) and Sustained Phonation (PHON). Experiments are conducted using speech data recorded from 60 ALS, 60 PD and60 HC subjects. Classification using SVM and DNN are considered baseline schemes.

When emotion recognition systems are used in new domains, the classification performance usually drops due to mismatches between training and testing conditions. Annotations of new data in the new domain is expensive and time demanding. Therefore, it is important to design strategies that efficiently use limited amount of new data to improve the robustness of the classification system. The use of ensembles is an attractive solution, since they can be built to perform well across different mismatches. The key challenge is to create ensembles that are diverse.

The performance of speech emotion classifiers greatly degrade when the training conditions do not match the testing conditions. This problem is observed in cross-corpora evaluations, even when the corpora are similar. The lack of generalization is particularly problematic when the emotion classifiers are used in real applications. This study addresses this problem by combining active learning (AL) and supervised domain adaptation (DA) using an elegant approach for support vector machine (SVM).

Deep neural networks have been proven to be very effective in various classification problems and show great promise for emotion recognition from speech. Studies have proposed various architectures that further improve the performance of emotion recognition systems. However, there are still various open questions regarding the best approach to building a speech emotion recognition system. Would the system’s performance improve if we have more labeled data? How much do we benefit from data augmentation? What activation and regularization schemes are more beneficial?