Although the UA-Speech and TORGO databases of control and dysarthric speech are invaluable resources made available to the research community with the objective of developing robust automatic speech recognition systems, they have also been used to validate a considerable number of automatic dysarthric speech classification approaches. Such approaches typically rely on the underlying assumption that recordings from control and dysarthric speakers are collected in the same noiseless environment using the same recording setup. In this paper, we show that this assumption is violated for the UA-Speech and TORGO databases. Using voice activity detection to extract speech and non-speech segments, we show that the majority of state-of-the-art dysarthria classification approaches achieve the same or a considerably better performance when using the non-speech segments of these databases than when using the speech segments. These results demonstrate that such approaches trained and validated on the UA-Speech and TORGO databases are potentially learning characteristics of the recording environment or setup rather than dysarthric speech characteristics. We hope that these results raise awareness in the research community about the importance of the quality of recordings when developing and evaluating automatic dysarthria classification approaches.

Mainstream deep learning-based dysarthric speech detection approaches typically rely on processing the magnitude spectrum of the short-time Fourier transform of input signals, while ignoring the phase spectrum. Although considerable insight about the structure of a signal can be obtained from the magnitude spectrum, the phase spectrum also contains inherent structures which are not immediately apparent due to phase discontinuity. To reveal meaningful phase structures, alternative phase representations such as the modified group delay (MGD) and instantaneous frequency (IF) spectra have been investigated in several applications. The objective of this paper is to investigate the applicability of the unprocessed phase, MGD, and IF spectra for dysarthric speech detection. Experimental results show that dysarthric cues are present in all considered phase representations. Further, it is shown that using phase representations as complementary features to the magnitude spectrum is beneficial for deep learning-based dysarthric speech detection, with the combination of magnitude and IF spectra yielding a high performance. The presented results should raise awareness in the research community about the potential of the phase spectrum for dysarthric speech detection and motivate research into novel architectures which optimally exploit magnitude and phase information.

Altered quality of the phonetic-acoustic information in the speech signal in the case of motor speech disorders may reduce its intelligibility. Monitoring intelligibility is part of the standard clinical assessment of patients. It is also a valuable tool to index the evolution of the speech disorder. However, measuring intelligibility raises methodological debates concerning: the type of linguistic material on which the assessment is based (non-words, words, continuous speech), the evaluation protocol and type of scores (scale-based rating, transcription or recognition tests), and the advantages and disadvantages of listener vs. automatic-based approaches (subjective vs. objective, expertise level, types of models used). In this paper, the intelligibility of the speech of 32 French patients presenting mild to moderate dysarthria and 17 elderly speakers is assessed with five different methods: impressionistic clinician judgment on continuous speech, number of words recognized in an interactive face-to-face setting and in an on-line testing of the same material by 75 judges, automatic feature-based and automatic speech recognition-based methods (both on short sentences). The implications of the different methods for clinical practice are discussed.

Speech representations which are robust to pathology-unrelated cues such as speaker identity information have been shown to be advantageous for automatic dysarthric speech classification. A recently proposed technique to learn speaker identity-invariant representations for dysarthric speech classification is based on adversarial training. However, adversarial training can be challenging, unstable, and sensitive to training parameters. To avoid adversarial training, in this paper we propose to learn speaker-identity invariant representations exploiting a feature separation framework relying on mutual information minimization. Experimental results on a database of neurotypical and dysarthric speech show that the proposed adversarial-free framework successfully learns speaker identity-invariant representations. Further, it is shown that such representations result in a similar dysarthric speech classification performance as the representations obtained using adversarial training, while the training procedure is more stable and less sensitive to training parameters.

Automatic dysarthric speech detection can provide reliable and cost-effective computer-aided tools to assist the clinical diagnosis and management of dysarthria. In this paper we propose a novel automatic dysarthric speech detection approach based on analyses of pairwise distance matrices using convolutional neural networks (CNNs). We represent utterances through articulatory posteriors and consider pairs of phonetically-balanced representations, with one representation from a healthy speaker (i.e., the reference representation) and the other representation from the test speaker (i.e., test representation). Given such pairs of reference and test representations, features are first extracted using a feature extraction front-end, a frame-level distance matrix is computed, and the obtained distance matrix is considered as an image by a CNN-based binary classifier. The feature extraction, distance matrix computation, and CNN-based classifier are jointly optimized in an end-to-end framework. Experimental results on two databases of healthy and dysarthric speakers for different languages and pathologies show that the proposed approach yields a high dysarthric speech detection performance, outperforming other CNN-based baseline approaches.

To assist the clinical diagnosis and treatment of speech dysarthria, automatic dysarthric speech detection techniques providing reliable and cost-effective assessment are indispensable. Based on clinical evidence on spectro-temporal distortions associated with dysarthric speech, we propose to automatically discriminate between healthy and dysarthric speakers exploiting spectro-temporal subspaces of speech. Spectro-temporal subspaces are extracted using singular value decomposition, and dysarthric speech detection is achieved by applying a subspace-based discriminant analysis. Experimental results on databases of healthy and dysarthric speakers for different languages and pathologies show that the proposed subspace-based approach using temporal subspaces is more advantageous than using spectral subspaces, also outperforming several state-of-the-art automatic dysarthric speech detection techniques.

Automatic pathological speech intelligibility measures are crucial to assist the clinical diagnosis and treatment of speech disorders. The recently proposed pathological short-time objective intelligibility (P-ESTOI) measure was shown to be very advantageous, yielding a high performance for several speech pathologies. However, to assess the intelligibility of an utterance from a patient, P-ESTOI relies on the availability of recordings of the same utterance by several healthy speakers such that an intelligible reference model can be created. Such recordings are not always easily available, limiting the practical applicability of P-ESTOI. To be able to use P-ESTOI in such scenarios, in this paper we propose to use synthetic speech generated by state-of-the-art high-quality text-to-speech systems to create an intelligible reference model. Experimental results on a database of Cerebral Palsy patients show that the performance of P-ESTOI using synthetic speech references is comparable to using natural speech references, making P-ESTOI a flexible measure which does not require healthy speech recordings and which outperforms state-of-the-art pathological speech intelligibility measures.

Competitive state-of-the-art automatic pathological speech intelligibility measures typically rely on regression training on a large number of features, require a large amount of healthy speech training data, or are applicable only to phonetically balanced scenarios where healthy and pathological speakers utter the same utterances. As a result, their performance in unseen data is unsatisfactory, and they cannot be used in low-resource languages or in phonetically unbalanced scenarios. To overcome these drawbacks, we propose a subspace-based intelligibility (SBI) measure. The SBI measure operates based on the hypothesis that dominant spectral patterns of pathological speech differ from intelligible speech (where the pathological and intelligible speech signals do not need to match in phonetic content), with the difference increasing as pathological speech intelligibility decreases. The SBI measure uses a minimal number of speech recordings to compute dominant spectral basis vectors spanning intelligible and pathological speech. The subspaces spanned by the intelligible and pathological spectral basis vectors are compared to each other through a subspace distance measure, which is directly used (i.e., without any training) as the pathological speech intelligibility estimate. Exploiting psychoacoustic evidence on the importance of spectral modulation cues to the perceived speech intelligibility and clinical evidence on the degradation of these cues in pathological speech, we show that the power of the proposed SBI measure lies in capturing the effect of spectral modulation degradation. To be able to additionally track possible degradations in the temporal structure of the pathological speech signal, we also propose two extensions of the SBI measure by incorporating short-time temporal information. Experimental results for different languages and speech pathologies show that the proposed intelligibility measures yield high and significant correlations with subjective intelligibility ratings, while not requiring any regression training or a large number of healthy speech recordings and being applicable to phonetically unbalanced scenarios.

Impaired speech intelligibility in motor speech disorders arising due to neurological diseases negatively affects the communication ability and quality of life of patients. Reliable and cost-effective measures to automatically assess speech intelligibility are necessary for the management of such disorders. In this paper, we propose to automatically assess the intelligibility of pathological speech based on short-time objective intelligibility measures typically used in speech enhancement, which however require a reference signal that is time-aligned to the test signal. We propose a method to create an utterance-dependent reference signal of intelligible speech from multiple healthy speakers. In order to assess intelligibility, the pathological speech signal is aligned to the created reference signal using dynamic time warping and the divergence between the two signals is quantified using either the short-time or the spectral correlation. Experiments on databases of English and French patients suffering from Cerebral Palsy and Amyotrophic Lateral Sclerosis show that the proposed intelligibility measures can obtain a high correlation with subjective intelligibility ratings, outperforming several state-of-the-art pathological speech intelligibility measures.

Speech intelligibility is an important assessment criterion of the communicative performance of pathological speakers. To assist clinicians in their assessment, time- and cost-efficient automatic intelligibility measures offering a repeatable and reliable assessment are desired. In this paper, we propose to automatically assess pathological speech intelligibility based on a distance measure between the subspaces of spectral patterns of the pathological speech signal and of a fully intelligible (healthy) speech signal. To extract the subspace of spectral patterns we investigate two linear decomposition methods, i.e., Principal Component Analysis and Approximate Joint Diagonalization. Pathological speech intelligibility is then derived using a Grassman distance measure which quantifies the difference between the extracted subspaces of pathological and healthy speech. Experiments on an English database of Cerebral Palsy patients show that the proposed intelligibility measure is significantly correlated with subjective intelligibility ratings. In addition, comparisons to state-of-the-art measures show that the proposed subspace-based measure achieves a high performance with a significantly lower computational cost and without imposing any constraints on the speech material of the speakers.