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Using Multivariate Statistics

https://schererstefan.net/assets/files/papers/1-s2.0-S0167639315000369-main.pdf

A review of depression and suicide risk assessment using speech analysis

Speech processing algorithms are often developed demonstrating improvements over the state-of-the-art, but sometimes at the cost of high complexity. This makes algorithm reimplementations based on literature difficult, and thus reliable comparisons between published results and current work are hard to achieve. This paper presents a new collaborative and freely available repository for speech processing algorithms called COVAREP, which aims at fast and easy access to new speech processing algorithms and thus facilitating research in the field. We envisage that COVAREP will allow more reproducible research by strengthening complex implementations through shared contributions and openly available code which can be discussed, commented on and corrected by the community. Presently COVAREP contains contributions from five distinct laboratories and we encourage contributions from across the speech processing research field. In this paper, we provide an overview of the current offerings of COVAREP and also include a demonstration of the algorithms through an emotion classification experiment.

/pdf/covarep-a-collaborative-voice-analysis-repository-for-speech-4ovkoxkaly.pdf

COVAREP — A collaborative voice analysis repository for speech technologies

1. Random Variables and Stochastic Processes

This paper describes an hidden Markov model (HMM)-based speech synthesizer that utilizes glottal inverse filtering for generating natural sounding synthetic speech. In the proposed method, speech is first decomposed into the glottal source signal and the model of the vocal tract filter through glottal inverse filtering, and thus parametrized into excitation and spectral features. The source and filter features are modeled individually in the framework of HMM and generated in the synthesis stage according to the text input. The glottal excitation is synthesized through interpolating and concatenating natural glottal flow pulses, and the excitation signal is further modified according to the spectrum of the desired voice source characteristics. Speech is synthesized by filtering the reconstructed source signal with the vocal tract filter. Experiments show that the proposed system is capable of generating natural sounding speech, and the quality is clearly better compared to two HMM-based speech synthesis systems based on widely used vocoder techniques.

/pdf/hmm-based-speech-synthesis-utilizing-glottal-inverse-1zho2kntvb.pdf

HMM-Based Speech Synthesis Utilizing Glottal Inverse Filtering

The electromyographic (EMG) signal provides information about the performance of muscles and nerves. At any instant, the shape of the muscle signal, motor unit action potential (MUAP), is constant unless there is movement of the position of the electrode or biochemical changes in the muscle due to changes in contraction level. The rate of neuron pulses, whose exact times of occurrence are random in nature, is related to the time duration and force of a muscle contraction. The EMG signal can be modeled as the output signal of a filtered impulse process where the neuron firing pulses are assumed to be the input of a system whose transfer function is the motor unit action potential. Representing the neuron pulses as a point process with random times of occurrence, the higher order statistics based system reconstruction algorithm can be applied to the EMG signal to characterize the motor unit action potential. In this paper, we report results from applying a cepstrum of bispectrum based system reconstruction algorithm to real wired-EMG (wEMG) and surface-EMG (sEMG) signals to estimate the appearance of MUAPs in the Rectus Femoris and Vastus Lateralis muscles while the muscles are at rest and in six other contraction positions. It is observed that the appearance of MUAPs estimated from any EMG (wEMG or sEMG) signal clearly shows evidence of motor unit recruitment and crosstalk, if any, due to activity in neighboring muscles. It is also found that the shape of MUAPs remains the same on loading.

Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential

Glottal inverse filtering is of potential use in a wide range of speech processing applications. As the process of voice production is, to a first order approximation, a source-filter process, then obtaining source and filter components provides for a flexible representation of the speech signal for use in processing applications. In certain applications the desire for accurate inverse filtering is more immediately obvious, e.g., in the assessment of laryngeal aspects of voice quality and for correlations between acoustics and vocal fold dynamics, the resonances of the vocal tract should firstly be removed. Similarly, for assessment of vocal performance, trained singers may wish to obtain quantitative data or feedback regarding their voice at the level of the larynx.

/pdf/a-review-of-glottal-waveform-analysis-3ja4ekeh61.pdf

A review of glottal waveform analysis

Signals from extracellular electrodes in neural systems record voltages resulting from activity in many neurons. Detecting action potentials (spikes) in a small number of specific (target) neurons is difficult because many neurons, both near and more distant, contribute to the signal at the electrode. We consider some nearby neurons as target neurons (providing a signal) and all the other contributions to the signal as noise. A new algorithm for spike detection has been developed: this applies a cepstrum of bispectrum (CoB) estimated inverse filter to provide blind equalization. This technique is based on higher order statistics, and seeks to find a sequence of event times or delta sequence. We show that the CoB-based technique can achieve a 98% hit rate on an extracellular signal containing three spike trains at up to 0 dB SNR. Threshold setting for this technique is discussed, and we show the application of the technique to some real signals. We compare performance with four established techniques and report that the CoB-based algorithm performs best.

/pdf/a-new-spike-detection-algorithm-for-extracellular-neural-1jgddw5qco.pdf

A New Spike Detection Algorithm for Extracellular Neural Recordings

This paper examines the use of Mel-frequency Cepstral Coefficients in the classification of musical instruments. 2004 piano, violin and flute samples are analysed to get their coefficients. These coefficients are reduced using principal component analysis and used to train a multi-layered perceptron. The network is trained on the first 3, 4 and 5 principal components calculated from the envelope of the changes in the coefficients. This trained network is then used to classify novel input samples. By training and testing the network on a different number of coefficients, the optimum number of coefficients to include for identifying a musical instrument is determined. We conclude that using 4 principal components from the first 15 coefficients gives the most accurate classification results.

The use of mel-frequency cepstral coefficients in musical instrument identification

We characterize a proposed metastability measurement system in which asynchronous data input and sampling clock frequencies trigger metastability. We develop an equation describing the time interval between data and clock inputs for practical frequencies and show that it takes on discrete values in the absence of jitter and that the presence of jitter perturbs these values. Finally, we present experimental results supporting our characterization

Jacqueline Walker

Papers

Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential

A review of glottal waveform analysis

A New Spike Detection Algorithm for Extracellular Neural Recordings

The use of mel-frequency cepstral coefficients in musical instrument identification

Characterization of a Flip-Flop Metastability Measurement Method