Speech processing

Automatic recognition of emotions from speech

Human emotions detection considering speech signals is a field that has attracted the attention of the research community since the last years. Several situations where the human integrity and security is at risk have been addressed; particularly the analysis of speech in emergency calls or in call-centers, are an interesting scenario. This project aimed to develop a methodology to classify different types of emotions such as anger, anxiety, disgust, and desperation, in scenarios where the speech signal is contaminated with noise or is coded by telephone channels.

2016 Third Frederick Jelinek Memorial Summer Workshop

Remote Monitoring of Neurodegeneration through Speech

TAPAS

Training Network on Automatic Processing of PAthological Speech

Feature Space Visualization with Spatial Similarity Maps for Pathological Speech Data

The feature vectors of a data set encode information about relations between speaker groups, clusters and outliers. Based on the assumption that these relations are conserved within the spatial properties of feature vectors, we introduce similarity …

Phonet: A Tool Based on Gated Recurrent Neural Networks to Extract Phonological Posteriors from Speech

There are a lot of features that can be extracted from speech signals for different applications such as automatic speech recognition or speaker verification. However, for pathological speech processing there is a need to extract features about the …

Surgical mask detection with deep recurrent phonetic models

To solve the task of surgical mask detection from audio recordings in the scope of Interspeech’s ComParE challenge, we introduce a phonetic recognizer which is able to differentiate between clear and mask samples. A deep recurrent phoneme recognition …

Automated Cross-language Intelligibility Analysis of Parkinson’s Disease Patients Using Speech Recognition Technologies

Speech deficits are common symptoms amongParkinson’s Disease (PD) patients. The automatic assessment of speech signals is promising for the evaluation of the neurological state and the speech quality of the patients. Recently, progress has been made …

Speech differences between CI users with pre-and postlingual onset of deafness detected by speech processing methods on voiceless to voice transitions

Introduction: The onset of deafness affects speech in different ways. Speech differences of Cochlear Implant (CI) users with pre- and postlingual deafness are examined using acoustic features extracted automatically from speech. Methods: Utterances …

A Multitask Learning Approach to Assess the Dysarthria Severity in Patients with Parkinson's Disease

Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor and non-motor symptoms. Particularly, several speech impairments appear in the initial stages of the disease, which affect aspects related to respiration and the …

Apkinson: A Mobile Solution for Multimodal Assessment of Patients with Parkinson's Disease

Parkinson’s disease is a neurological disorder that produces different motor impairments in the patients. The longitudinal assessment of the neurological state of patients is important to improve their quality of life. We introduced Apkinson, a …