Developing automatic methods to detect Parkinson's disease (PD) from speech has attracted increasing interest as these techniques can potentially be used in telemonitoring health applications. This article studies the utilization of voice source information in the detection of PD using two classifier architectures: traditional pipeline approach and end-to-end approach. The former consists of feature extraction and classifier stages. In feature extraction, the baseline acoustic features-consisting of articulation, phonation, and prosody features-were computed and voice source information was extracted using glottal features that were estimated by iterative adaptive inverse filtering (IAIF) and quasi-closed phase (QCP) glottal inverse filtering methods. Support vector machine classifiers were developed utilizing the baseline and glottal features extracted from every speech utterance and the corresponding healthy/PD labels. The end-to-end approach uses deep learning models which were trained using both raw speech waveforms and raw voice source waveforms. In the latter, two glottal inverse filtering methods (IAIF and QCP) and zero frequency filtering method were utilized. The deep learning architecture consists of a combination of convolutional layers followed by a multilayer perceptron. Experiments were performed using PC-GITA speech database. From the traditional pipeline systems, the highest classification accuracy (67.93%) was given by combination of baseline and QCP-based glottal features. From the end-to-end-systems, the highest accuracy (68.56%) was given by the system trained using QCP-based glottal flow signals. Even though classification accuracies were modest for all systems, the study is encouraging as the extraction of voice source information was found to be most effective in both approaches.

Dysarthria is a neuro-motor disorder resulting from the disruption of normal activity in speech production leading to slow, slurred and imprecise (low intelligible) speech. Automatic classification of dysarthria from speech can be used as a potential clinical tool in medical treatment. This paper examines the effectiveness of glottal source parameters in dysarthric speech classification from three categories of speech signals, namely non-words, words and sentences. In addition to the glottal parameters, two sets of acoustic parameters extracted by the openSMILE toolkit are used as baseline features. A dysarthric speech classification system is proposed by training support vector machines (SVMs) using features extracted from speech utterances and their labels indicating dysarthria/healthy. Classification accuracy results indicate that the glottal parameters contain discriminating information required for the identification of dysarthria. Additionally, the complementary nature of the glottal parameters is demonstrated when these parameters, in combination with the openSMILE-based acoustic features, result in improved classification accuracy. Analysis of classification accuracies of the glottal and openSMILE features for non-words, words and sentences is carried out. Results indicate that in terms of classification accuracy the word level is best suited in identifying the presence of dysarthria.

In speech analysis, the information about the glottal source is obtained from speech by using glottal inverse filtering (GIF). The accuracy of state-of-the-art GIF methods is sufficiently high when the input speech signal is of high-quality (i.e., with little noise or reverberation). However, in realistic conditions, particularly when GIF is computed from coded telephone speech, the accuracy of GIF methods deteriorates severely. To robustly estimate the glottal source under coded condition, a deep neural network (DNN)-based method is proposed. The proposed method utilizes a DNN to map the speech features extracted from the coded speech to the glottal flow waveform estimated from the corresponding clean speech. To generate the coded telephone speech, adaptive multi-rate (AMR) codec is utilized which is a widely used speech compression method. The proposed glottal source estimation method is compared with two existing GIF methods, closed phase covariance analysis (CP) and iterative adaptive inverse filtering (IAIF). The results indicate that the proposed DNN-based method is capable of estimating glottal flow waveforms from coded telephone speech with a considerably better accuracy in comparison to CP and IAIF.

This paper proposes a new approach of modeling the excitation signal as deterministic and noise components. Initially, a study on characteristics of excitation or residual signal around glottal closure instant (GCI) is performed using principal component analysis (PCA). Based on the study, the segment of residual signal around GCI is considered as the deterministic component and the remaining part of the residual signal is considered as the noise component. The deterministic component is parameterized using PCA coefficients, and the noise component can be represented in terms of spectral and amplitude envelopes. The proposed excitation modeling approach is incorporated in the HMM-based speech synthesis system. Subjective evaluation results show a significant improvement in the quality of speech synthesized by the proposed method, compared to three existing methods.

This paper describes an online laboratory for a course on electronic devices and circuits aimed at complementing classroom teaching of undergraduate courses. The development is based on four levels of teaching, viz., lecture notes, interactive tutorials, simulation exercises, and performing a real experiment remotely. The current facility has been developed to support an undergraduate theory course, “Electronic Devices and Circuits” as a pre-laboratory session for a regular on-site laboratory. It can be also used as an alternative to a traditional laboratory especially for distance education programs, or for interdisciplinary first year courses not having lab component. The facility has also been developed as a teaching tool for classroom demonstrations.