12 accepted papers
Existing methods for analyzing linguistic content from picture descriptions for assessment of cognitive-linguistic impairment often overlook the participant’s visual narrative path, which typically requires eye tracking to assess. Spatio-semantic graphs are a useful tool for analyzing this narrative…
Hypernasality, a speech resonance disorder characterized by excessive nasal airflow, presents challenges in accurate detection across languages. Traditional assessments of hypernasality include perceptual evaluation and nasometry. More recently, objective acoustic measures based on formant analysis…
Recent applications of decorrelation methods to the multi-head attention layers and output embeddings of transformer-based models have resulted in improvements in efficiency and accuracy. Despite these advancements, there is a lack of research focused on the influence of decorrelation on transformer…
Training robust clinical speech-based models that generalize requires large sample sizes because speech is variable and high-dimensional. Researchers have turned to foundational models, such as the Bidirectional Encoder Representations from Transformers (BERT), to generate lower-dimensional embeddin…
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…
Hypernasality refers to the perception of excessive nasal resonance during the production of oral sounds. Existing methods for automatic assessment of hypernasality from speech are based on machine learning models trained on disordered speech databases rated by speech-language pathologists. However,…
Detecting early signs of neurodegeneration is vital for planning treatments for neurological diseases. Speech plays an important role in this context because it has been shown to be a promising early indicator of neurological decline, and because it can be acquired remotely without the need for spec…
Hypernasal speech is a common symptom across several neurological disorders; however it has a variable acoustic signature, making it difficult to quantify acoustically or perceptually. In this paper, we propose the nasal cognate distinctiveness features as an objective proxy for hypernasal speech. O…
Training machine learning algorithms for speech applications requires large, labeled training data sets. This is problematic for clinical applications where obtaining such data is prohibitively expensive because of privacy concerns or lack of access. As a result, clinical speech applications typical…
Instrumental analysis of speech sometimes complements subjective evaluations in speech and language therapy; however, apart from elemental speech features such as pitch and formant statistics, higher dimensional spectral features are rarely used in practice because they are clinically uninterpretabl…
Objective assessment of pathological speech is an important part of existing systems for automatic diagnosis and treatment of various speech disorders. In this paper, we propose a new regression method for this application. Rather than treating speech samples from each speaker as individual data ins…
In regression analysis, outliers in the data can induce a bias in the learned function, resulting in larger errors. In this paper we derive an empirically estimable bound on the regression error based on a Euclidean minimum spanning tree generated from the data. Using this bound as motivation, we pr…