Discriminative metric design for pattern recognition

Simultaneous positive sequential vectors modeling and unsupervised feature selection via continuous hidden Markov models

In order to make Intelligent Transportation System (ITS) work effectively, a driver intention recognition method is proposed. In this research, three different recognition models were developed based on Continuous Hidden Markov Model (CHMM), and could distinguish left and right lane change intention from normal lane keeping intention. Subjects performed lane change maneuvers and lane keeping maneuvers with driving simulator which simulated highway scenes, parameters that highly correlated with lane change behavior were collected and analyzed. A series of testings and comparisons were done to obtain the optimal model structure and feature set. Results show that, taking the steering wheel angel, steering wheel angle velocity and lateral acceleration as the optimal observation signals, the accuracy can achieve up 95%, and it proved very effective in terms of early intention recognition.

This paper presents a parallel phoneme recognition algorithm based on the continuous Hidden Markov Model (HMM). The parallel phoneme recognition algorithm distributes 3-state HMMs of context dependent phonemes to the multiprocessors, computes output probabilities in parallel, and enhances the Viterbi beam search with a message passing mechanism. The algorithm is implemented in a multi-transputer system using distributed-memory MIMD multiprocessors. Experimental results show the feasibility of the parallel phoneme recognition algorithm in constructing a real-time parallel speech recognition system based on time-consuming continuous HMM.

We propose new continuous hidden Markov model (CHMM) structure that integrates feature weighting component. We assume that each feature vector could include different subsets of features that come from different sources of information or different feature extractors. We modify the probability density function that characterizes the standard CHMM to include state and component dependent feature relevance weights. To learn the optimal feature weights from the training data, we modify the maximum likelihood based Baum-Welch algorithm and we derive the necessary conditions. The proposed approach is validated using synthetic and real data sets. The results are shown to outperform the standard CHMM.

In spotting for phrases in text images, speed and accuracy are important considerations. In a hidden Markov model (HMM) based spotter recognition time is dominated by the time required to compute the state conditional observation probabilities. These probabilities are a measure of how well the data match each state in the model. In this paper discrete and continuous hidden Markov models are compared based on speed and accuracy in spotting for phrases in text images. For the discrete HMM, vector quantization is used to associate each continuous feature vector with a discrete value. For the continuous HMMs, the observation distributions for the feature vectors are modeled by either a single Gaussian, or a mixture of two Gaussians. Comparisons were made on a subset of the UW English Document Image Database I. The best accuracy was observed when a mixture of two Gaussians was used in the continuous HMM. The discrete HMM provides for faster spotting particularly when long phrases are used.

Multi metric functional connectivity analysis based on continuous hidden Markov model with application in early diagnosis of Alzheimer’s disease

Due to the rapid advances in algorithms, VLSI design and computer technology, security systems based on speaker recognition are on the verge of commercial success. In this thesis, an improved strategy for Text Tndependent Automatic Speaker Verification (TI-ASV) system based on Malayalam has been proposed and results are observed. The system performs on Gaussian Mixture Model (GMM) technique with cepstral based features. Different speech pre-processing techniques like pre-emphasis filtering, frame blocking and windowing have been used to process the speech utterances. MFCC, ΔMFCC and ΔΔMFCC have been used to extract the features. Speaker identification is performed using Continuous Hidden Markov Model. The performance is analyzed in terms of Percentage Correctness (PC) and accuracy. An application with Graphical User Interface (GUI) is also developed for security purposes using the system. The system is developed using the framework of Hidden Markov Model Tool Kit (HTK).

It is important to diagnose the bearing fault to prevent the serious accident of equipment. This paper introduces a bearing fault identification scheme based on envelope power spectrum analysis, linear discriminate analysis and continuous hidden Markov model. First the envelope power spectrum features are extracted from amplitude demodulated vibration signals from fault bearings. Then, linear discriminate analysis is employed to reduce the feature dimensions, which are helpful for improving the computing speed and diagnosing accuracy. At last, the new linear discriminate analysis features are input into continuous hidden Markov model to train the models under different conditions, respectively. In bearing fault identification, test data are input into the pretrained continuous hidden Markov models, and the bearing state can be detected by the output of continuous hidden Markov model. To validate the effectiveness of the proposed method, experimental samples of four bearing conditions at different fault sizes and loads are utilized to test the continuous hidden Markov model and back-propagation neural network. The result shows that continuous hidden Markov model and linear discriminate analysis-based method have higher accuracy and efficiency than back-propagation neural network.

We propose a new model for speaker-independent vowel recognition which uses the flexibility of the dynamic linking that results from the synchronization of oscillating neural units. The system consists of an input layer and three neural layers, which are referred to as the A-, B- and C-centers. The input signals are a time series of linear prediction (LPC) spectrum envelopes of auditory signals. At each time-window within the series, the A-center receives input signals and extracts local peaks of the spectrum envelope, i.e., formants, and encodes them into local groups of independent oscillations. Speaker-independent vowel characteristics are embedded as a connection matrix in the B-center according to statistical data of Japanese vowels. The associative interaction in the B-center and reciprocal interaction between the A- and B-centers selectively activate a vowel as a global synchronized pattern over two centers. The C-center evaluates the synchronized activities among the three formant regions to give the selective output of the category among the five Japanese vowels. Thus, a flexible ability of dynamical linking among features is achieved over the three centers. The capability in the present system was investigated for speaker-independent recognition of Japanese vowels. The system demonstrated a remarkable ability for the recognition of vowels very similar to that of human listeners, including misleading vowels. In addition, it showed stable recognition for unsteady input signals and robustness against background noise. The optimum condition of the frequency of oscillation is discussed in comparison with stimulus-dependent synchronizations observed in neurophysiological experiments of the cortex.

Sequentially spherical data modeling with hidden Markov models and its application to fMRI data analysis

A possible interaction of (volcano-) tectonic earthquakes with the continuous seismic noise recorded in the volcanic island of Tenerife was recently suggested, but existing catalogues seem to be far from being self consistent, calling for the development of automatic detection and classification algorithms. In this work we propose the adoption of a methodology based on Hidden Markov Models (HMMs), widely used already in other fields, such as speech classification.

The purpose of this work is to show the results obtained when the latest technological advances in the area of Automatic Speech Recognition (ASR) are applied to the Western-Huastec Nahuatl and Huastec languages. Western-Huastec Nahuatl and Huastec are not only native (indigenous) languages in Mexico, but also minority languages, and people who speak these languages usually are analphabetic. A speech database was created by recording the voice of native speaker when reading a set of documents used for native bilingual primary school in the official mexican state education system. A pronunciation dictionary was created for each language. A continuous Hidden Markov Models (HMM) were used for acoustical modeling, and bigrams were used for language Modeling. A Viterbi decoder was used for recognition. The word error rate of this task is below 8.621% for Western-Huastec Nahuatl language and 10.154% for Huastec language.

In this paper, a new mimesis scheme is proposed. This scheme enables for a humanoid to imitate human's motion even though the humanoid cannot see human's whole-body motion and the humanoid has not seen the exactly same motion so far. Mimesis framework is based on continuous hidden Markov model. Viterbi algorithm is applied in order to generate more various motion patterns than the number of existing hidden Markov models. In order to imitate other's motion in a smooth way, a smoothing technique in generation problem is realized. The feasibility of this method is demonstrated by simulation on a 20 degrees of freedom humanoid robot configuration

In this chapter, a speech recognition system is proposed to recognize spoken words using a continuous hidden Markov model (HMM). Three datasets of human sounds are collected and used to train and test the model. Features are extracted from the sound data and used to categorize each sound into one of a set of classes. The output is a word in the dictionary. Both person-dependent and person-independent speech recognition are explored. A detailed experiment is performed, and successful recognition results are reported. The study highlights how Arabic words containing certain sounds are recognized differently and distinguishes the specific characteristics of hidden Markov models necessary to recognize Arabic as opposed to English words.

The Industrial Internet of Things (IIOT) can help the manufacturing enterprises improve the production efficiency, reduce costs and achieve intelligent factory production. In this harsh scenario, however, the electromagnetic noise performs a severe impact on IIOT which uses low-power wireless sensor devices. In this paper, we aim to characterize electromagnetic noise for IIoT systems based on the realistic measurement data collected in an automobile factory. The measurements were conducted in time and frequency domain, respectively. By using the frequency-domain measurement data, we can extract the frequency occupation and power amplitude information of electromagnetic noise between 300 MHz to 3 GHz band. By using the time-domain measurement data, the noise amplitude distribution (NAD) of electromagnetic noise at 315MHz, 779MHz and 916MHz bands are extracted. Finally, a continuous hidden Markov model (CHMM) is used to characterize the time-varying property of electromagnetic noise. These results are beneficial and informative when designing wireless networks for the IIoT.