Teager energy based feature parameters for speech recognition in car noise

Cataloged from PDF version of article.In this letter, a new set of speech feature parameters based on multirate signal processing and the Teager energy operator is introduced. The speech signal is first divided into nonuniform subbands in mel-scale using a multirate filterbank, then the Teager energies of the subsignals are estimated. Finally, the feature vector is constructed by log-compression and inverse discrete cosine transform (DCT) computation. The new feature parameters have robust speech recognition performance in the presence of car engine noise

Quantitative Assessment of Motor Deficit with an Intelligent Key Object: A Pilot Study

International audienceConventional assessment of sensorimotor functions is carried out using standard clinical scales which are subjective and insufficiently sensitive to changes in motor performance. Alternatively, sensor based systems offer a quantitative approach to motor assessment. We have designed a set of low cost, easy to use instrumented objects to assess a subject's performance during skilled tasks. In this pilot study we discuss the design of one object, the intelligent key, and describe how it can be used to assess a subject's performance during fine manipulation tasks using the proposed metrics and techniques. Three subjects with motor disability and one healthy subject participated in this study. Subjects performed insertion and rotation tasks that mimic the skills used in day to day key manipulation. A threshold detector algorithm based on Teager Energy Operator was applied to the object acceleration signal to quantify time spent struggling with the task and Spectral Arc Length was used to assess the smoothness of pronation/supination. Overall, the results indicate that increased difficulty in task performance correlates with decreased smoothness in task performance

Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics

An algorithm was developed to separate pistachio nuts with closed-shells from those with open-shells. It was observed that upon impact on a steel plate, nuts with closed-shells emit different sounds than nuts with open-shells. Two feature vectors extracted from the sound signals were melcepstrum coefficients and eigenvalues obtained from the principle component analysis of the autocorrelation matrix of the signals. Classification of a sound signal was done by linearly combining feature vectors from both mel-cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable. During the training phase, sounds of the nuts with closed-shells and open-shells were used to obtain a representative vector of each class. The accuracy of closed-shell nuts was more than 99% on the test set

COMPUTER BASED VOICE ANALYSIS ON MEDICAL DIAGNOSIS

Sesin oluşmasını sağlayan organlarındaki patolojik durumlardan kaynaklanan ses hastalıklarının birçoğu sesin kalitesinde değişime sebep olur. Uzmanlar, sesteki hastalıklara tanı koymak için değişik yöntemler kullanmaktadır. Bu çalışmada; örselemesiz tabanlı analiz ile, doktorun tanı koymasına yardımcı olunmaktadır. Genlik değişim oranı, perde değişim oranı, sessizlik derecesi, Teager enerji ortalamalı dalgacık dönüşüm katsayıları ve yüksek dereceli istatistik parametreleri ile öznitelik vektörleri oluşturulmuştur. Sağlıklı veya farklı hastalık sınıflarına ait ses bölütleri, geriye yayınım temelli çok katmanlı algılayıcı ağlar ile sınıflandırılmıştır. Geriye yayınım temelli ağlar; esnek, ölçekli-eşlenik gradyan ve Brodyen-Fletcher-Goldfarb-Shanno (BFGS) öğrenme algoritmaları ile eğitilmiştir. Benzetim çalışmaları sonucunda, ölçekli-eşlenik gradyan algoritmasının en iyi sonucu verdiği bulunmuştur. The change in voice quality is affected by many of voice disorders that coming from pathogical conditions of voice generation organs. The aim of this study is to help that the clinicians could be diagnosed about voice disorders with non-invasive based analysis. In our work, amplitude perturbation quotient, pitch period perturbation quotient, degree of unvoiceness, Teager Energy Operators averages of wavelet transform coefficients, and higher-order statistics parameters have formed the feature vectors. The voice segments belonging to different pathological or normal classes were classified by backpropagation based multilayer perceptron networks. In backpropagation based multilayer perceptron networks, resilient, scaled-conjugate gradient, and Brodyen-Fletcher-Goldfarb-Shanno learning algorithms were used in training. According to the results of the simulation studies, scaled-conjugate gradient algorithm gave the best results

Assessment of Variability in Irregularly Sampled Time Series: Applications to Mental Healthcare

Variability is defined as the propensity at which a given signal is likely to change. There are many choices for measuring variability, and it is not generally known which ones offer better properties. This paper compares different variability metrics applied to irregularly (nonuniformly) sampled time series, which have important clinical applications, particularly in mental healthcare. Using both synthetic and real patient data, we identify the most robust and interpretable variability measures out of a set 21 candidates. Some of these candidates are also proposed in this work based on the absolute slopes of the time series. An additional synthetic data experiment shows that when the complete time series is unknown, as it happens with real data, a non-negligible bias that favors normalized and/or metrics based on the raw observations of the series appears. Therefore, only the results of the synthetic experiments, which have access to the full series, should be used to draw conclusions. Accordingly, the median absolute deviation of the absolute value of the successive slopes of the data is the best way of measuring variability for this kind of time series

Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm

Speech recognition is an important technology and can be used as a great aid for individuals with sight or hearing disabilities today. There are extensive research interest and development in this area for over the past decades. However, the prospect in Malaysia regarding the usage and exposure is still immature even though there is demand from the medical and healthcare sector. The aim of this research is to assess the quality and the impact of using computerized method for early screening of speech articulation disorder among Malaysian such as the omission, substitution, addition and distortion in their speech. In this study, the statistical probabilistic approach using Hidden Markov Model (HMM) has been adopted with newly designed Malay corpus for articulation disorder case following the SAMPA and IPA guidelines. Improvement is made at the front-end processing for feature vector selection by applying the silence region calibration algorithm for start and end point detection. The classifier had also been modified significantly by incorporating Viterbi search with Genetic Algorithm (GA) to obtain high accuracy in recognition result and for lexical unit classification. The results were evaluated by following National Institute of Standards and Technology (NIST) benchmarking. Based on the test, it shows that the recognition accuracy has been improved by 30% to 40% using Genetic Algorithm technique compared with conventional technique. A new corpus had been built with verification and justification from the medical expert in this study. In conclusion, computerized method for early screening can ease human effort in tackling speech disorders and the proposed Genetic Algorithm technique has been proven to improve the recognition performance in terms of search and classification task

Novel Methods for Forensic Multimedia Data Analysis: Part I

The increased usage of digital media in daily life has resulted in the demand for novel multimedia data analysis techniques that can help to use these data for forensic purposes. Processing of such data for police investigation and as evidence in a court of law, such that data interpretation is reliable, trustworthy, and efficient in terms of human time and other resources required, will help greatly to speed up investigation and make investigation more effective. If such data are to be used as evidence in a court of law, techniques that can confirm origin and integrity are necessary. In this chapter, we are proposing a new concept for new multimedia processing techniques for varied multimedia sources. We describe the background and motivation for our work. The overall system architecture is explained. We present the data to be used. After a review of the state of the art of related work of the multimedia data we consider in this work, we describe the method and techniques we are developing that go beyond the state of the art. The work will be continued in a Chapter Part II of this topic