Automatic segmentation in speech synthesis

Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

In the past years, Hidden Markov Models have been used in several fields and applications with success. More recently, these models have been applied to improve the reliability of a machinery system. In many cases, failure is preceded by specific sequences of events (signature), which can be detected by an adequate Hidden Markov Model (HMM). Classical laws like lifetime models or survival functions are used to estimate the lifetime of a system. The default of these laws is that only the elapsed time is used to estimate the end of life of a system. The aim of this paper is to validate an HMM approach. We first use a synthetic HMM model of degradation to produce event sequences. This synthetic model has been inspired by a real process. In this case, we can adjust the failure rate by changing model parameters. All the parameters of this synthetic model are known and provide references which can be evaluated by different indicators. Classical survival functions used in reliability are computed on synthetic sequences. These laws validate the behavior of the synthetic model. The higher the failure rate, the shorter the lifetime duration. These results confirm that a four-state, left to right, HMM topology can represent the degradation level of a system. In a second time, this HMM approach is used in a real case, where degradation levels are unknown. Degradation estimates are compared with the results from classical survival functions used in the first case. Then we show that the degradation level provided by the HMM approach is more efficient than the survival functions approach. The HMM approach takes into account the events collected about a system, not only the elapsed time as is the case with survival functions.

This paper introduces two unsupervised learning methods for analyzing functional magnetic resonance imaging (fMRI) data based on hidden Markov model (HMM). HMM approach is focused on capturing the first-order statistical evolution among the samples of a voxel time series, and it can provide a complimentary perspective of the BOLD signals. Two-state HMM is created for each voxel, and the model parameters are estimated from the voxel time series and the stimulus paradigm. Two different activation detection methods are presented in this paper. The first method is based on the likelihood and likelihood-ratio test, in which an additional Gaussian model is used to enhance the contrast of the HMM likelihood map. The second method is based on certain distance measures between the two state distributions, in which the most likely HMM state sequence is estimated through the Viterbi algorithm. The distance between the on-state and off-state distributions is measured either through a t-test, or using the Kullback-Leibler distance (KLD). Experimental results on both normal subject and brain tumor subject are presented. HMM approach appears to be more robust in detecting the supplemental active voxels comparing with SPM, especially for brain tumor subject.

Sleep quantity affects an individual's personal health. The gold standard of measuring sleep and diagnosing sleep disorders is Polysomnography (PSG). Although PSG is accurate, it is expensive and it lacks portability. A number of wearable devices with embedded sensors have emerged in the recent past as an alternative to PSG for regular sleep monitoring directly by the user. These devices are intrusive and cause discomfort besides being expensive. In this work, we present an algorithm to detect sleep using a smartphone with the help of its inbuilt accelerometer sensor. We present three different approaches to classify raw acceleration data into two states - Sleep and Wake. In the first approach, we take an equation from Kushida's algorithm to process accelerometer data. Henceforth, we call it Kushida's equation. While the second is based on statistical functions, the third is based on Hidden Markov Model (HMM) training. Although all the three approaches are suitable for a phone's resources, each approach demands different amount of resources. While Kushida's equation-based approach demands the least, the HMM training-based approach demands the maximum. We collected data from mobile phone's accelerometer for four subjects for twelve days each. We compare accuracy of sleep detection using each of the three approaches with that of Zeo sensor, which is based on Electroencephalogram (EEG) sensor to detect sleep. EEG is an important modality in PSG. We find that HMM training-based approach is as much as 84% accurate. It is 15% more accurate as compared to Kushida's equation-based approach and 10% more accurate as compared to statistical method-based approach. In order to concisely represent the sleep quality of people, we model their sleep data using HMM. We present an analysis to find out a tradeoff between the amount of training data and the accuracy provided in the modeling of sleep. We find that six days of sleep data is sufficient for accurate modeling. We compare accuracy of our HMM training-based algorithm with a representative third party app SleepTime available from Google Play Store for Android. We find that the detection done using HMM approach is closer to that done by Zeo by 13% as compared to the third party Android application SleepTime. We show that our HMM training-based approach is efficient as it takes less than ten seconds to get executed on Moto G Android phone.

In this paper, a flame detection system based on a pyroelectric (or passive) infrared (PIR) sensor is described. The flame detection system can be used for fire detection in large rooms. The flame flicker process of an uncontrolled fire and ordinary activity of human beings are modeled using a set of hidden Markov models (HMM), which are trained using the wavelet transform of the PIR sensor signal. Whenever there is an activity within the viewing range of the PIR sensor system, the sensor signal is analyzed in the wavelet domain and the wavelet signals are fed to a set of HMMs. A fire or no fire decision is reached according to the HMM producing the highest probability.

We present a path planning framework that takes into account the human's safety perception in the presence of a flying robot. The framework addresses two objectives: (i) estimation of the uncertain parameters of the proposed safety perception model based on test data collected using Virtual Reality (VR) testbed, and (ii) offline optimal control computation using the estimated safety perception model. Due to the unknown factors in the human tests data, it is not suitable to use standard regression techniques that minimize the mean squared error (MSE). We propose to use a Hidden Markov model (HMM) approach where human's attention is considered as a hidden state to infer whether the data samples are relevant to learn the safety perception model. The HMM approach improved log-likelihood over the standard least squares solution. For path planning, we use Bernstein polynomials for discretization, as the resulting path remains within the convex hull of the control points, providing guarantees for deconfliction with obstacles at low computational cost. An example of optimal trajectory generation using the learned human model is presented. The optimal trajectory generated using the proposed model results in reasonable safety distance from the human. In contrast, the paths generated using the standard regression model have undesirable shapes due to overfitting. The example demonstrates that the HMM approach has robustness to the unknown factors compared to the standard MSE model.

Masquerade detection is the ability to detect attackers known as masqueraders that intrude on another user’s system and pose as legitimate users. Once a masquerader obtains access to a user’s system, the masquerader has free reign over whatever data is on that system. In this research, we focus on masquerade detection and user classi cation using the following two di erent approaches: the heavy hitter approach and 2 di erent approaches based on hidden Markov models (HMMs), the dueling-HMM and threshold-HMM strategies. The heavy hitter approach computes the frequent elements seen in the training data sequence and test data sequence and computes the distance to see whether the test data sequence is masqueraded or not. The results show very misleading classi cations, suggesting that the approach is not viable for masquerade detection. A hidden Markov model is a tool for representing probability distributions over sequences of observations [9]. Previous research has shown that using a threshold-based hidden Markov model (HMM) approach is successful in a variety of categories: malware detection, intrusion detection, pattern recognition, etc. We have veri ed that using a threshold-based HMM approach produces high accuracy with low amounts of a false positives. Using the dueling- HMM approach, which utilizes multiple training HMMs, we obtain an overall accuracy of 81.96%. With the introduction of the bias in the dueling-HMM approach, we produce similar results to the results obtained in the threshold-based HMM approach, where we see many non-masqueraded data detected, while many masqueraded data avoid detection, yet still result in an high overall accuracy.

This paper proposes an approach combining the set of Krawtchouk moments and HMMs (Hidden Markov Models) for unconstrained offline handwritten Chinese character recognition. The Krawtchouk moments can be effectively used as pattern features in analysis of two-dimensional images. Since the basis set is orthogonal in the discrete domain of the image coordinate space, the implementation of Krawtchouk moments doesn't involve any numerical approximation or coordinate transformation. This property makes Krawtchouk moments superior to conventional, more continuous moments such as Legendre moments and Zernike moments. In the field of character recognition, HMMs architecture has been used widely because of its strong adaptability to written distortion and variability. In this paper, Krawtchouk moments and HMMs are combined to recognize unconstrained offline handwritten Chinese amount in words. Experiments show that the average recognition of Krawtchouk is higher than Zernike and Legendre moments within the HMM framework.

In this paper we study the automatic recognition of sound categories (such as fridge, mixers or sawing sounds) from their vocal imitations. Vocal imitations are made of a succession over time of sounds produced using vocal mechanisms that can largely differ from the ones used in speech. We develop here a recognition approach inspired by automatic-speech-recognition systems, with an acoustic model (that maps the audio signal to a set of probability over “phonemes”) and a language model (that represents the expected succession of “phonemes” for each sound category). Since we do not know what are the underlying “phonemes” of vocal imitations we propose to automatically estimate them using Shift-Invariant Probabilistic Latent Component Analysis (SI-PLCA) applied to a dataset of vocal imitations. The kernel distributions of the SI-PLCA are considered as the “phonemes” of vocal imitation and its impulse distributions are used to compute the emission probabilities of the states of a set of Hidden Markov Models (HMMs). To evaluate our proposal, we test it for a task of automatically recognizing 12 sound categories from their vocal imitations.

Restructuring Gaussian mixture density functions in speaker-independent acoustic models

In this chapter we introduce an original approach to predict a day-ahead energy demand, based on machine learning and pattern recognition. Our Hidden Markov Model (HMM) is a simple and explainable model that uses integer sequences to define emission probability distributions attached to states. We develop a Mathematica code which relies on the maximum likelihood principle in HMM environment. Based on these exploratory results, we conclude that the HMM approach is an efficient way in modeling short-term/day-ahead energy demand prediction, especially during peak period(s) and in accounting for the inherent stochastic nature of demand conditions. The model can be easily extended to predict energy demand values for more than one day in the future. However, the accuracy of such predictions would decrease as we expect.

The most important challenge of the spectrum sensing in cognitive radio (CR) is to find a way to share the licensed spectrum without interfering with the licensed user transmission. Predicting the near future of the licensed or primary user (PU) channel state can solve this problem. Many studies have investigated the primary user channel state prediction in recent literature, in this study we introduce new approach for PU channel state prediction in time domain based on hidden Markov model (HMM) and Markov switching model (MSM). In our new approach we use a time series to capture the primary user channel state detection sequence (PU channel “idle” or “occupied”). Then, we fed this time series as an observation sequence into HMM and MSM algorithm to predict the switching time between the two states, “idle and occupied” before it happens so that the secondary user (SU) can adjust its transmission strategies accordingly. The experimental results show that all HMM and MSM perform very well for PU channel state prediction. It has also shown in a simulation comparison between HMM and MSM algorithm that MSM algorithm can preform without need for training process and provide a smoother prediction with low computational complexity than HMM.

In this research, a novel feature set is used to automatically segment speech signal. Automatic segmentation is very useful especially for large database. A hybrid features model is created from wavelet packet analysis and mel-scale is used to train Hidden Markov Model (HMM) for phone boundary detection. HMM is implemented using the Hidden Markov Model Toolkit (HTK).The database (Ked-TIMIT) is used for result verifications and Mel Frequency Cepstral Coefficients (MFCC) is used as reference for evaluating the results of the proposed Hybrid model. The results are categorized for vowels, consonants and short phones. Phone duration and start location are used as metrics to evaluate the system success rate. Success rate of 74% is achieved for consonant detection, 72% for vowel detection and 58% for short phone detection. Using the simple metric that relies only on boundary locations but ignoring duration, the achieved results are 92.5% for consonant detection, 90% for vowel detection and 77.5% for short phoneme detection. In addition to boundary detection the proposed hybrid model is utilized to compare newly developed features called Mel scale Best Tree Encoding (Mel-BTE ) to the mostly used popular features MFCC along with all experiments using the same database. The relative results forMel-BTE with respect to MFCC are 94.77% for consonant detection, 87.5% for vowel detection and 93.33% for short phoneme detection.

A hybrid approach of NN and HMM for facial emotion classification

Using IDE usage data to analyze the behavior of software developers in the field, during the course of their daily work, can lend support to (or dispute) laboratory studies of devel- opers. This paper describes a technique that leverages Hidden Markov Models (HMMs) as a means of mining high-level developer behavior from low-level IDE interaction traces of many developers in the field. HMMs use dual stochastic processes to model higher-level hidden behavior using observable input sequences of events. We propose an interactive approach of mining interpretable HMMs, based on guiding a human expert in building a high quality HMM in an iterative, one state at a time, manner. The final result is a model that is both representative of the field data and captures the field phenomena of interest. We apply our HMM construction approach to study debugging behavior, using a large IDE interaction dataset collected from nearly 200 developers at ABB, Inc. Our results highlight the different modes and constituent actions in debugging, exhibited by the developers in our dataset.