∏ = i i e e (2.1.1) Das bedeutet, dass in einem Multiband-Modell der Gesamtfehler immer kleiner ist als der kleinste Teilbandfehler. Die Teilbandfehler werden durch das SNR in den Teilbändern bestimmt i SNR i e e min = (siehe Anhang B). Formuliert man (2.1.1) mit der Phonemerkennrate e s − = 1 und logarithmiert diesen Ausdruck, erhält man die gewünschte Additivität: ∑ − = − i i s s) 1 (log) 1 (...

High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of lines is extremely for the safe operation systems. With development modern data acquisition deep learning technology, methods have feasibility engineering application location. The traditional single-terminal traveling w...

Sustained resonance in a linear oscillator is achievable with a drive whose constant frequency matches the resonant frequency of the oscillator. But in oscillators with nonlinear restoring forces such as the pendulum, Duffing and Duffing-Van der Pol oscillator, the resonant frequency changes as the amplitude changes, so a constant frequency drive results in a beat oscillation instead of sustain...

Dysarthria is a motor speech disorder that impacts verbal articulation and co-ordination, resulting in slow, slurred and imprecise speech. Automated classification of dysarthria subtypes and severities could provide a useful clinical tool in assessing the onset and progress in treatment. This study represents a pilot project to train models to detect the presence of dysarthria in continuous spe...

Speech production variations due to perceptually induced stress contribute signiicantly to reduced speech processing performance. One approach that can improve the robustness of speech processing (e.g., recognition) algorithms against stress is to formulate an objective classiication of speaker stress based upon the acoustic speech signal. In this paper , an overview of recent methods for stres...

There are many stressful environments which deteriorate the performance of speech recognition systems. Examples include aircraft cockpits, 911 emergency telephone response, high workload task stress, or emotional situations. To address this, we investigate a number of linear and nonlinear features and processing methods for stressed speech classi cation. The linear features include properties o...

In this paper we overview emotional speech recognition having in mind three goals. The first goal is to provide an up-todate record of the available emotional speech data collections. The number of emotional states, the language, the number of speakers, and the kind of speech are briefly addressed. The second goal is to present the most frequent acoustic features used for emotional speech recog...

In this paper, we propose a robust voice activity detection (VAD) algorithm to effectively distinguish speech from non-speech in various noisy environments. The proposed VAD utilizes power spectral deviation (PSD), using Teager energy (TE) to provide a better representation of the PSD, resulting in improved decision performance for speech segments. In addition, the TE-based likelihood ratio and...