物件導向軟體之架構(Object-Oriented Software Construction)探討

An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of Baum and Petrie (1966) on finite-state finite-alphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a general alphabet. In particular, statistical properties and ergodic theorems for relative entropy densities of HMPs were developed. Consistency and asymptotic normality of the maximum-likelihood (ML) parameter estimator were proved under some mild conditions. Similar results were established for switching autoregressive processes. These processes generalize HMPs. New algorithms were developed for estimating the state, parameter, and order of an HMP, for universal coding and classification of HMPs, and for universal decoding of hidden Markov channels. These and other related topics are reviewed.

http://www.isib.cnr.it/control/finesso/Ephraim_hmm.pdf

Hidden Markov processes

Hidden Markov model speech recognition systems typically use Gaussian mixture models to estimate the distributions of decorrelated acoustic feature vectors that correspond to individual subword units. By contrast, hybrid connectionist-HMM systems use discriminatively-trained neural networks to estimate the probability distribution among subword units given the acoustic observations. In this work we show a large improvement in word recognition performance by combining neural-net discriminative feature processing with Gaussian-mixture distribution modeling. By training the network to generate the subword probability posteriors, then using transformations of these estimates as the base features for a conventionally-trained Gaussian-mixture based system, we achieve relative error rate reductions of 35% or more on the multicondition Aurora noisy continuous digits task.

/pdf/tandem-connectionist-feature-extraction-for-conventional-hmm-3wwllmh0yp.pdf

Tandem connectionist feature extraction for conventional HMM systems

Clustering algorithms conduct a search through the space of possible organizations of a data set. In this paper, we propose two types of instance-level clustering constraints { must-link and cannot-link constraints { and show how they can be incorporated into a clustering algorithm to aid that search. For three of the four data sets tested, our results indicate that the incorporation of surprisingly few such constraints can increase clustering accuracy while decreasing runtime. We also investigate the relative eects of each type of constraint and nd that the type that contributes most to accuracy improvements depends on the behavior of the clustering algorithm without constraints.

Clustering with Instance-Level Constraints

The aim of this paper is to investigate the feasibility of an audio-based context recognition system. Here, context recognition refers to the automatic classification of the context or an environment around a device. A system is developed and compared to the accuracy of human listeners in the same task. Particular emphasis is placed on the computational complexity of the methods, since the application is of particular interest in resource-constrained portable devices. Simplistic low-dimensional feature vectors are evaluated against more standard spectral features. Using discriminative training, competitive recognition accuracies are achieved with very low-order hidden Markov models (1-3 Gaussian components). Slight improvement in recognition accuracy is observed when linear data-driven feature transformations are applied to mel-cepstral features. The recognition rate of the system as a function of the test sequence length appears to converge only after about 30 to 60 s. Some degree of accuracy can be achieved even with less than 1-s test sequence lengths. The average reaction time of the human listeners was 14 s, i.e., somewhat smaller, but of the same order as that of the system. The average recognition accuracy of the system was 58% against 69%, obtained in the listening tests in recognizing between 24 everyday contexts. The accuracies in recognizing six high-level classes were 82% for the system and 88% for the subjects.

Audio-based context recognition

Automatic classification of environmental noise events by hidden markov models

The automatic classification of environmental noise sources from their acoustic signatures recorded at the microphone of a noise monitoring system (NMS) is an active subject of research nowadays. This paper shows how hidden Markov models (HMMs) can be used to build an environmental noise recognition system based on a time-frequency analysis of the noise signal. The performance of the proposed HMM-based approach is evaluated experimentally for the classification of five types of noise events (car, truck, moped, aircraft, train). The HMM-based approach is found to outperform previously proposed classifiers based on the average spectrum of the noise event with more than 95% of correct classifications. For comparison, a classification test is performed with human listeners for the same data which shows that the best HMM-based classifier outperforms the "average" human listener who achieves only 91.8% of correct classification for the same task.

/pdf/automatic-classification-of-environmental-noise-events-by-2szeteph25.pdf

Automatic classification of environmental noise events by hidden Markov models

In this paper, we evaluate multi-Gaussian HMM systems and hybrid HMM/ANN systems in the framework of task independent training for small size (75 words) and medium size (600 words) vocabularies. To do this, we use the Phonebook database (Pitrelli et al., 1995) which is particularly well suited to this kind of experiment since (1) it is a very large telephone database and (2) the size and content of the test vocabulary is very flexible. For each system, different HMM topologies are compared to test the influence of state tying (with a number of parameters approximately kept constant) on the recognition performance. Two lexica (Phonebook and CMU) are also compared and it is shown that the CMU lexicon leads to significantly better performance. Finally, it is shown that with a quite simple system and a few adaptations to the basic HMM/ANN scheme, recognition performance of 98.5% and 94.7% can easily be achieved, respectively on a lexicon of 75 and 600 words (isolated words, telephone speech and lexicon words not present in the training data).

/pdf/hybrid-hmm-ann-systems-for-training-independent-tasks-1maaf5qarq.pdf

Hybrid HMM/ANN systems for training independent tasks: experiments on Phonebook and related improvements

Nonlinear discriminant analysis for improved speech recognition.

This paper presents a speaker dependent speech recognition with application to voice dialling. This work has been developed under the constraints imposed by voice dialling applications, i.e., low memory requirements and limited training material. Two methods for producing speaker dependent word baseforms based on phone-like units (PLU) are presented and compared: (1) a classical vector quantizer is used to divide the space into regions associated with PLUs; (2) a speaker independent hybrid HMM/MLP recognizer is used to generate speaker dependent PLU based models. This work shows that very low error rates can be achieved even with very simple systems, namely a DTW-based recognizer. However, the best results are achieved when using the hybrid HMM/MLP system to generate the word baseforms. Finally, a real-time demonstration simulating voice dialling functions and including keyword spotting and rejection capabilities has been set up and can be tested online.

http://publications.idiap.ch/downloads/papers/1997/voicedial.pdf

Vincent Fontaine

Papers

Automatic classification of environmental noise events by hidden markov models

Automatic classification of environmental noise events by hidden Markov models

Hybrid HMM/ANN systems for training independent tasks: experiments on Phonebook and related improvements

Nonlinear discriminant analysis for improved speech recognition.

Speaker-dependent speech recognition based on phone-like units models-application to voice dialling