Tobias Moosmayr

On the Necessity and Feasibility of Detecting a Driver's Emotional State While Driving

This paper brings together two important aspects of the human-machine interaction in cars: the psychological aspect and the engineering aspect. The psychologically motivated part of this study addresses questions such as whyit is important to automatically assess the drivers affective state, which states are important and how a machines response should look like. The engineering part studies howthe emotional state of a driver can be estimated by extracting acoustic features from the speech signal and mapping them to an emotion state in a multidimensional, continuous-valued emotion space. Such a feasibility study is performed in an experiment in which spontaneous, authentic emotional utterances are superimposed by car noise of several car types and various road surfaces.

Recognition of noisy speech: a comparative survey of robust model architecture and feature enhancement

Performance of speech recognition systems strongly degrades in the presence of background noise, like the driving noise inside a car. In contrast to existing works, we aim to improve noise robustness focusing on all major levels of speech recognition: feature extraction, feature enhancement, speech modelling, and training. Thereby, we give an overview of promising auditory modelling concepts, speech enhancement techniques, training strategies, and model architecture, which are implemented in an in-car digit and spelling recognition task considering noises produced by various car types and driving conditions. We prove that joint speech and noise modelling with a Switching Linear Dynamic Model (SLDM) outperforms speech enhancement techniques like Histogram Equalisation (HEQ) with a mean relative error reduction of 52.7% over various noise types and levels. Embedding a Switching Linear Dynamical System (SLDS) into a Switching Autoregressive Hidden Markov Model (SAR-HMM) prevails for speech disturbed by additive white Gaussian noise.

Switching Linear Dynamic Models for Noise Robust In-Car Speech Recognition

Performance of speech recognition systems strongly degrades in the presence of background noise, like the driving noise in the interior of a car. We compare two different Kalman filtering approaches which attempt to improve noise robustness: Switching Linear Dynamic Models (SLDM) and Autoregressive Switching Linear Dynamical Systems (AR-SLDS). Unlike previous works which are restricted on considering white noise, we evaluate the modeling concepts in a noisy speech recognition task where also colored noise produced through different driving conditions and car types is taken into account. Thereby we demonstrate that speech enhancement based on Kalman filtering prevails over all standard de-noising techniques considered herein, such as Wiener filtering, Histogram Equalization, and Unsupervised Spectral Subtraction.