Prediction of Speech Intelligibility by Means of EEG Responses to Sentences in Noise.

Objective: Understanding speech in noisy conditions is challenging even for people with mild hearing loss, and intelligibility for an individual person is usually evaluated by using several subjective test methods. In the last few years, a method has been developed to determine a temporal response function (TRF) between speech envelope and simultaneous electroencephalographic (EEG) measurements. By using this TRF it is possible to predict the EEG signal for any speech signal.

Extracting the Auditory Attention in a Dual-Speaker Scenario From EEG Using a Joint CNN-LSTM Model.

Human brain performs remarkably well in segregating a particular speaker from interfering ones in a multispeaker scenario. We can quantitatively evaluate the segregation capability by modeling a relationship between the speech signals present in an auditory scene, and the listener's cortical signals measured using electroencephalography (EEG). This has opened up avenues to integrate neuro-feedback into hearing aids where the device can infer user's attention and enhance the attended speaker.

Inference of the Selective Auditory Attention Using Sequential LMMSE Estimation.

Attentive listening in a multispeaker environment such as a cocktail party requires suppression of the interfering speakers and the noise around. People with normal hearing perform remarkably well in such situations. Analysis of the cortical signals using electroencephalography (EEG) has revealed that the EEG signals track the envelope of the attended speech stronger than that of the interfering speech.

An LMMSE-based Estimation of Temporal Response Function in Auditory Attention Decoding.

One of the remarkable abilities of humans is to focus the attention on a certain speaker in a multi-speaker environment that is known as the cocktail party effect. How the human brain solves this non-trivial task is a challenge that the scientific community has not yet found answers to. In recent years, progress has been made thanks to the development of system identification method based on least-squares (LS) that maps the variations between the cortical signals of a listener and the speech signals present in an auditory scene.