An investigation into glottal waveform based speech coding

Coding of voiced speech by extraction of the glottal waveform has shown promise in improving the efficiency of speech coding systems. This thesis describes an investigation into the performance of such a system. The effect of reverberation on the radiation impedance at the lips is shown to be negligible under normal conditions. Also, the accuracy of the Image Method for adding artificial reverberation to anechoic speech recordings is established. A new algorithm, Pre-emphasised Maximum Likelihood Epoch Detection (PMLED), for Glottal Closure Instant detection is proposed. The algorithm is tested on natural speech and is shown to be both accurate and robust. Two techniques for giottai waveform estimation, Closed Phase Inverse Filtering (CPIF) and Iterative Adaptive Inverse Filtering (IAIF), are compared. In tandem with an LF model fitting procedure, both techniques display a high degree of accuracy However, IAIF is found to be slightly more robust. Based on these results, a Glottal Excited Linear Predictive (GELP) coding system for voiced speech is proposed and tested. Using a differential LF parameter quantisation scheme, the system achieves speech quality similar to that of U S Federal Standard 1016 CELP at a lower mean bit rate while incurring no extra delay

Estimation of glottal closure instants in voiced speech using the DYPSA algorithm

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GCI DETECTION FROM RAW SPEECH USING A FULLY-CONVOLUTIONAL NETWORK

Glottal Closure Instants (GCI) detection consists in automatically detecting temporal locations of most significant excitation of the vocal tract from the speech signal. It is used in many speech analysis and processing applications, and various algorithms have been proposed for this purpose. Recently, new approaches using convo-lutional neural networks have emerged , with encouraging results. Following this trend, we propose a simple approach that performs a regression from the speech waveform to a target signal from which the GCI are easily obtained by peak-picking. However, the ground truth GCI used for training and evaluation are usually extracted from EGG signals, which are not reliable and often not available. To overcome this problem, we propose to train our network on high-quality synthetic speech with perfect ground truth. The performances of the proposed algorithm are compared with three other state-of-the-art approaches using publicly available datasets, and the impact of using controlled synthetic or real speech signals in the training stage is investigated. The experimental results demonstrate that the proposed method obtains similar or better results than other state-of-the-art algorithms and that using large synthetic datasets with many speaker offers better generalization ability than using a smaller database of real speech and EGG signals

Fundamental frequency estimation of low-quality electroglottographic signals

Fundamental frequency (fo) is often estimated based on electroglottographic (EGG) signals. Due to the nature of the method, the quality of EGG signals may be impaired by certain features like amplitude or baseline drifts, mains hum or noise. The potential adverse effects of these factors on fo estimation has to date not been investigated. Here, the performance of thirteen algorithms for estimating fo was tested, based on 147 synthesized EGG signals with varying degrees of signal quality deterioration. Algorithm performance was assessed through the standard deviation σfo of the difference between known and estimated fo data, expressed in octaves. With very few exceptions, simulated mains hum, and amplitude and baseline drifts did not influence fo results, even though some algorithms consistently outperformed others. When increasing either cycle-to-cycle fo variation or the degree of subharmonics, the SIGMA algorithm had the best performance (max. σfo = 0.04). That algorithm was however more easily disturbed by typical EGG equipment noise, whereas the NDF and Praat's auto-correlation algorithms performed best in this category (σfo = 0.01). These results suggest that the algorithm for fo estimation of EGG signals needs to be selected specifically for each particular data set. Overall, estimated fo data should be interpreted with care

DeepVOX: Discovering Features from Raw Audio for Speaker Recognition in Degraded Audio Signals

Automatic speaker recognition algorithms typically use pre-defined filterbanks, such as Mel-Frequency and Gammatone filterbanks, for characterizing speech audio. The design of these filterbanks is based on domain-knowledge and limited empirical observations. The resultant features, therefore, may not generalize well to different types of audio degradation. In this work, we propose a deep learning-based technique to induce the filterbank design from vast amounts of speech audio. The purpose of such a filterbank is to extract features robust to degradations in the input audio. To this effect, a 1D convolutional neural network is designed to learn a time-domain filterbank called DeepVOX directly from raw speech audio. Secondly, an adaptive triplet mining technique is developed to efficiently mine the data samples best suited to train the filterbank. Thirdly, a detailed ablation study of the DeepVOX filterbanks reveals the presence of both vocal source and vocal tract characteristics in the extracted features. Experimental results on VOXCeleb2, NIST SRE 2008 and 2010, and Fisher speech datasets demonstrate the efficacy of the DeepVOX features across a variety of audio degradations, multi-lingual speech data, and varying-duration speech audio. The DeepVOX features also improve the performance of existing speaker recognition algorithms, such as the xVector-PLDA and the iVector-PLDA