Speech processing

On Modeling Glottal Source Information for Phonation Assessment in Parkinson’s Disease

Parkinson’s disease produces several motor symptoms, including different speech impairments that are known as hypokinetic dysarthria. Symptoms associated to dysarthria affect different dimensions of speech such as phonation, articulation, prosody, …

Influence of the Interviewer on the Automatic Assessment of Alzheimer's Disease in the Context of the ADReSSo Challenge.

Alzheimer’s Disease (AD) results from the progressive loss of neurons in the hippocampus, which affects the capability to produce coherent language. It affects lexical, grammatical, and semantic processes as well as speech fluency. This paper …

Parallel Representation Learning for the Classification of Pathological Speech: Studies on Parkinson’s Disease and Cleft Lip and Palate

Speech signals may contain different paralinguistic aspects such as the presence of pathologies that affect the proper communication capabilities of a speaker. Those speech disorders have different origin depending on the type of the disease. For …

Automatic detection of Voice Onset Time in voiceless plosives using gated recurrent units

Voice Onset Time (VOT) has been used by researchers as an acoustic measure in order to gain some understanding about the impact of different motor speech disorders in speech production. However, VOT values are usually obtained manually, which is …

Current methods and new trends in signal processing and pattern recognition for the automatic assessment of motor impairments: the case of Parkinson's disease

This chapter describes current methods and future trends in signal processing and pattern recognition techniques applied to the automatic analysis of motor impairments observed in patients with Parkinson's disease (PD). A revision of the …

Multi-channel spectrograms for speech processing applications using deep learning methods

Time–frequency representations of the speech signals provide dynamic information about how the frequency component changes with time. In order to process this information, deep learning models with convolution layers can be used to obtain feature …

Comparison of User Models Based on GMM-UBM and I-Vectors for Speech, Handwriting, and Gait Assessment of Parkinson’s Disease Patients

Parkinson’s disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Information from speech, handwriting, and gait signals have been considered to evaluate the neurological state of the patients. On the …

Apkinson: the smartphone application for telemonitoring Parkinson’s patients through speech, gait and hands movement

Aim: This paper introduces Apkinson, a mobile application for motor evaluation and monitoring of Parkinson’s disease patients. Materials & methods: The App is based on previously reported methods, for instance, the evaluation of articulation and …

Articulation and Empirical Mode Decomposition Features in Diadochokinetic Exercises for the Speech Assessment of Parkinson's Disease Patients

Speech impairments are one of the earliest manifestations in patients with Parkinson’s disease. Particularly, articulation impairments related to the capability of the speaker to move the limbs and muscles of the vocal tract have been observed in the …

Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson’s Disease from Speech in Three Different Languages

Parkinson’s disease patients develop different speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows the development of computer aided tools to support the diagnosis and the …