Maximum Likelihood Linear Transformation Research Articles

An HMM-DNN-Based System for the Detection and Classification of Low-Frequency Acoustic Signals from Baleen Whales, Earthquakes, and Air Guns off Chile

Marine passive acoustic monitoring can be used to study biological, geophysical, and anthropogenic phenomena in the ocean. The wide range of characteristics from geophysical, biological, and anthropogenic sounds sources makes the simultaneous automatic detection and classification of these sounds a significant challenge. Here, we propose a single Hidden Markov Model-based system with a Deep Neural Network (HMM-DNN) for the detection and classification of low-frequency biological (baleen whales), geophysical (earthquakes), and anthropogenic (air guns) sounds. Acoustic data were obtained from the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization station off Juan Fernandez, Chile (station HA03) and annotated by an analyst (498 h of audio data containing 30,873 events from 19 different classes), and then divided into training (60%), testing (20%), and tuning (20%) subsets. Each audio frame was represented as an observation vector obtained through a filterbank-based spectral feature extraction procedure. The HMM-DNN training procedure was carried out discriminatively by setting HMM states as targets. A model with Gaussian Mixtures Models and HMM (HMM-GMM) was trained to obtain an initial set of HMM target states. Feature transformation based on Linear Discriminant Analysis and Maximum Likelihood Linear Transform was also incorporated. The HMM-DNN system displayed good capacity for correctly detecting and classifying events, with high event-level accuracy (84.46%), high weighted average sensitivity (84.46%), and high weighted average precision (89.54%). Event-level accuracy increased with higher event signal-to-noise ratios. Event-level metrics per class also showed that our HMM-DNN system generalized well for most classes but performances were best for classes that either had a high number of training exemplars (e.g., generally above 50) and/or were for classes of signals that had low variability in spectral features, duration, and energy levels. Fin whale and Antarctic blue whale song and air guns performed particularly well.

A comparative study of deep neural network based Punjabi-ASR system

HMM is regarded as the leader from last five decades for handling the temporal variability in an input speech signal for building automatic speech recognition system. GMM became an integral part of HMM so as to measure the efficiency of each state that stores the information of a short windowed frame. In order to systematically fit the frame, it reserves the frame coefficients and connects their posterior probability over HMM state that acts as an output. In this paper, deep neural network (DNN) is tested against the GMM through utilization of many hidden layers which helps the DNN to successfully evade the issue of overfitting on large training dataset before its performance becomes worse. The implementation DNN with robust feature extraction approach has brought a high performance margin in Punjabi speech recognition system. For feature extraction, the baseline MFCC and GFCC approaches are integrated with cepstral mean and variance normalization. The dimension reduction, decorrelation of vector information and speaker variability is later addressed with linear discriminant analysis, maximum likelihood linear transformation, SAT, maximum likelihood linear regression adaptation models. Two hybrid classifiers investigate the conceived acoustic feature vectors: GMM–HMM, and DNN–HMM to obtain improvement in performance on connected and continuous Punjabi speech corpus. Experimental setup shows a notable improvement of 4–5% and 1–3% (in connected and continuous datasets respectively).

Maximum-Likelihood Linear Transformation for Unsupervised Domain Adaptation in Speaker Verification

Recent advances in front-end factor analysis through development of i-Vectors have led to significant gains in speaker recognition technology. However, the problem of mismatch between the domains of system development and evaluation data remains a challenging one. This domain mismatch occurs primarily because of the variability in the sources of development and evaluation data. In this study, we propose a novel method of unsupervised probabilistic feature transformation (UPFT) to reduce this domain mismatch by transforming an out-of-domain development data toward in-domain development data. We formulate the alignment of two different domains as a probability density estimation problem. We first train a Gaussian mixture model (GMM) using the out-of-domain i-Vectors. Next, we employ an expectation–maximization (EM) algorithm to fit the means of the GMM to the in-domain i-Vectors by maximizing the overall likelihood. At the optimum, the two domains become closer to each other in the i-Vector space. While reaching the optimum through multiple iterations of the EM, we reparameterize the centroid locations using the following set of transformation parameters: rotation, translation, and scaling. These transformation parameters, which are obtained during the optimization process, are later used to transform the out-of-domain i-Vectors toward in-domain i-Vectors. We observe that such a transformation leads to an improvement in performance of the out-of-domain speaker recognition system. Our proposed method has an added advantage of being completely unsupervised, and thus does not rely on any tuning parameters. We conduct experiments on both 2013 domain adaptation challenge corpus as well as National Institute of Standards and Technology Speaker Recognition Evaluation (SRE)—2016 corpus. On both corpora, we obtain significant improvements using the proposed UPFT solution. Specifically for the SRE-2016 corpus, using a cosine distance scoring based system, we are able to recover almost 90% of the performance gap between an in-domain and out-of-domain system.

A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosis

Health prognosis for equipment is considered as a key process of the condition-based maintenance strategy. This paper presents an integrated framework for multi-sensor equipment diagnosis and prognosis based on adaptive hidden semi-Markov model (AHSMM). Unlike hidden semi-Markov model (HSMM), the basic algorithms in an AHSMM are first modified in order for decreasing computation and space complexity. Then, the maximum likelihood linear regression transformations method is used to train the output and duration distributions to re-estimate all unknown parameters. The AHSMM is used to identify the hidden degradation state and obtain the transition probabilities among health states and durations. Finally, through the proposed hazard rate equations, one can predict the useful remaining life of equipment with multi-sensor information. Our main results are verified in real world applications: monitoring hydraulic pumps from Caterpillar Inc. The results show that the proposed methods are more effective for multi-sensor monitoring equipment health prognosis.

A Closed-Form Solution of Linear Spectral Transformation for Robust Speech Recognition

The maximum likelihood linear spectral transformation (ML-LST) using a numerical iteration method has been previously proposed for robust speech recognition. The numerical iteration method is not appropriate for real-time applications due to its computational complexity. In order to reduce the computational cost, the objective function of the ML-LST is approximated and a closed-form solution is proposed in this paper. It is shown experimentally that the proposed closed-form solution for the ML-LST can provide rapid speaker and environment adaptation for robust speech recognition.

The Nitech-NAIST HMM-Based Speech Synthesis System for the Blizzard Challenge 2006

We describe a statistical parametric speech synthesis system developed by a joint group from the Nagoya Institute of Technology (Nitech) and the Nara Institute of Science and Technology (NAIST) for the annual open evaluation of text-to-speech synthesis systems named Blizzard Challenge 2006. To improve our 2005 system (Nitech-HTS 2005), we investigated new features such as mel-generalized cepstrum-based line spectral pairs (MGC-LSPs), maximum likelihood linear transform (MLLT), and a full covariance global variance (GV) probability density function (pdf). A combination of mel-cepstral coefficients, MLLT, and full covariance GV pdf scored highest in subjective listening tests, and the 2006 system performed significantly better than the 2005 system. The Blizzard Challenge 2006 evaluations show that Nitech-NAIST-HTS 2006 is competitive even when working with relatively large speech databases.

Rapid adaptation using linear spectral transformation for embedded speech recognisers

Embedded speech recognisers are typically used in unknown mobile environments where the acoustic conditions frequently change. Since a large amount of adaptation data is not usually available for such environments, the adaptation methods for the acoustic models of these recognisers must improve the recognition performance with only a small amount of adaptation data. In this Letter, we show that maximum likelihood linear spectral transformation provides the advantage of rapid adaptation using a very limited amount of adaptation data for the embedded acoustic models.

On maximum mutual information speaker-adapted training

In this work, we combine maximum mutual information parameter estimation with speaker-adapted training (SAT). As will be shown, this can be achieved by performing unsupervised estimation of speaker adaptation parameters on the test data, a distinct advantage for many recognition tasks involving conversational speech. We derive re-estimation formulae for the basic speaker-independent means and variances, the optimal regression class for each Gaussian component when multiple speaker-dependent linear transforms are used for adaptation, as well as the optimal feature-space transformation matrix for use with semi-tied covariance matrices. We also propose an approximation to the maximum likelihood and maximum mutual information SAT re-estimation formulae that greatly reduces the amount of disk space required to conduct training on corpora which contain speech from hundreds or thousands of speakers. We also present empirical evidence of the importance of combination speaker adaptation with discriminative training. In particular, on a subset of the data used for the NIST RT05 evaluation, we show that including maximum likelihood linear regression transformations in the MMI re-estimation formulae provides a WER of 35.2% compared with 39.1% obtained when speaker adaptation is ignored during discriminative training.

Linear Spectral Transformation for Robust Speech Recognition Using Maximum Mutual Information

This paper presents a transformation-based rapid adaptation technique for robust speech recognition using a linear spectral transformation (LST) and a maximum mutual information (MMI) criterion. Previously, a maximum likelihood linear spectral transformation (ML-LST) algorithm was proposed for fast adaptation in unknown environments. Since the MMI estimation method does not require evenly distributed training data and increases the a posteriori probability of the word sequences of the training data, we combine the linear spectral transformation method and the MMI estimation technique in order to achieve extremely rapid adaptation using only one word of adaptation data. The proposed algorithm, called MMI-LST, was implemented using the extended Baum-Welch algorithm and phonetic lattices, and evaluated on the TIMIT and FFMTIMIT corpora. It provides a relative reduction in the speech recognition error rate of 11.1% using only 0.25 s of adaptation data.

Discriminative Estimation of Subspace Constrained Gaussian Mixture Models for Speech Recognition

In this paper, we study discriminative training of acoustic models for speech recognition under two criteria: maximum mutual information (MMI) and a novel "error-weighted" training technique. We present a proof that the standard MMI training technique is valid for a very general class of acoustic models with any kind of parameter tying. We report experimental results for subspace constrained Gaussian mixture models (SCGMMs), where the exponential model weights of all Gaussians are required to belong to a common "tied" subspace, as well as for subspace precision and mean (SPAM) models which impose separate subspace constraints on the precision matrices (i.e., inverse covariance matrices) and means. It has been shown previously that SCGMMs and SPAM models generalize and yield significant error rate improvements over previously considered model classes such as diagonal models, models with semitied covariances, and extended maximum likelihood linear transformation (EMLLT) models. We show here that MMI and error-weighted training each individually result in over 20% relative reduction in word error rate on a digit task over maximum-likelihood (ML) training. We also show that a gain of as much as 28% relative can be achieved by combining these two discriminative estimation techniques

Gaussian mixture models with covariances or precisions in shared multiple subspaces

We introduce a class of Gaussian mixture models (GMMs) in which the covariances or the precisions (inverse covariances) are restricted to lie in subspaces spanned by rank-one symmetric matrices. The rank-one basis are shared between the Gaussians according to a sharing structure. We describe an algorithm for estimating the parameters of the GMM in a maximum likelihood framework given a sharing structure. We employ these models for modeling the observations in the hidden-states of a hidden Markov model based speech recognition system. We show that this class of models provide improvement in accuracy and computational efficiency over well-known covariance modeling techniques such as classical factor analysis, shared factor analysis and maximum likelihood linear transformation based models which are special instances of this class of models. We also investigate different sharing mechanisms. We show that for the same number of parameters, modeling precisions leads to better performance when compared to modeling covariances. Modeling precisions also gives a distinct advantage in computational and memory requirements

Subspace constrained Gaussian mixture models for speech recognition

A standard approach to automatic speech recognition uses hidden Markov models whose state dependent distributions are Gaussian mixture models. Each Gaussian can be viewed as an exponential model whose features are linear and quadratic monomials in the acoustic vector. We consider here models in which the weight vectors of these exponential models are constrained to lie in an affine subspace shared by all the Gaussians. This class of models includes Gaussian models with linear constraints placed on the precision (inverse covariance) matrices (such as diagonal covariance, maximum likelihood linear transformation, or extended maximum likelihood linear transformation), as well as the LDA/HLDA models used for feature selection which tie the part of the Gaussians in the directions not used for discrimination. In this paper, we present algorithms for training these models using a maximum likelihood criterion. We present experiments on both small vocabulary, resource constrained, grammar-based tasks, as well as large vocabulary, unconstrained resource tasks to explore the rather large parameter space of models that fit within our framework. In particular, we demonstrate significant improvements can be obtained in both word error rate and computational complexity.

Model Enforcement: A Unified Feature Transformation Framework for Classification and Recognition

Bayesian classifiers rely on models of the a priori and class-conditional feature distributions; the classifier is trained by optimizing these models to best represent features observed in a training corpus according to certain criterion. In many problems of interest, the true class-conditional feature probability density function (PDF) is not a member of the set of PDFs the classifier can represent. Previous research has shown that the effect of this problem may be reduced either by improving the models or by transforming the features used in the classifier. This paper addresses this model mismatch problem in statistical identification, classification, and recognition systems. We formulate the problem as the problem of minimizing the relative entropy, which is also known as the Kullback-Leibler distance, between the true conditional PDF and the hypothesized probabilistic model. Based on this formulation, we provide a computationally efficient solution to the problem based on volume-preserving maps; existing linear transform designs are shown to be special cases of the proposed solution. Using this result, we propose the symplectic maximum likelihood transform (SMLT), which is a nonlinear volume-preserving extension of the maximum likelihood linear transform (MLLT). This approach has many applications in statistical modeling, classification, and recognition. We apply it to the maximum likelihood estimation (MLE) of the joint PDF of order statistics and show a significant increase in the likelihood for the same number of parameters. We provide also phoneme recognition experiments that show recognition accuracy improvement compared with using the baseline Mel-Frequency Cepstrum Coefficient (MFCC) features or using MLLT. We present an iterative algorithm to jointly estimate the parameters of the symplectic map and the probabilistic model for both applications.

Modeling Inverse Covariance Matricesby Basis Expansion

This paper proposes a new covariance modeling technique for Gaussian mixture models. Specifically the inverse covariance (precision) matrix of each Gaussian is expanded in a rank-1 basis i.e., /spl Sigma//sub j//sup -1/=P/sub j/=/spl Sigma//sub k=1//sup D//spl lambda//sub k//sup j/a/sub k/a/sub k//sup T/, /spl lambda//sub k//sup j//spl isin//spl Ropf/,a/sub k//spl isin//spl Ropf//sup d/. A generalized EM algorithm is proposed to obtain maximum likelihood parameter estimates for the basis set {a/sub k/a/sub k//sup T/}/sub k=1//sup D/ and the expansion coefficients {/spl lambda//sub k//sup j/}. This model, called the extended maximum likelihood linear transform (EMLLT) model, is extremely flexible: by varying the number of basis elements from D=d to D=d(d+1)/2 one gradually moves from a maximum likelihood linear transform (MLLT) model to a full-covariance model. Experimental results on two speech recognition tasks show that the EMLLT model can give relative gains of up to 35% in the word error rate over a standard diagonal covariance model, 30% over a standard MLLT model.

Maximum likelihood linear transformations for HMM-based speech recognition

This paper examines the application of linear transformations for speaker and environmental adaptation in an HMM-based speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Only model-based linear transforms are considered, since, for linear transforms, they subsume the appropriate feature–space transforms. The paper compares the two possible forms of model-based transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii) constrained, which requires the variance transform to have the same form as the mean transform. Re-estimation formulae for all appropriate cases of transform are given. This includes a new and efficient full variance transform and the extension of the constrained model–space transform from the simple diagonal case to the full or block–diagonal case. The constrained and unconstrained transforms are evaluated in terms of computational cost, recognition time efficiency, and use for speaker adaptive training. The recognition performance of the two model–space transforms on a large vocabulary speech recognition task using incremental adaptation is investigated. In addition, initial experiments using the constrained model–space transform for speaker adaptive training are detailed.