nils-herrmann/clean_scirepeval huggingface dataset:
CONSTRAINED MAXIMUM LIKELIHOOD MODELING WITH GAUSSIAN DISTRIBUTIONS

Maximum Likelihood (ML) modeling of multiclass data using gaussian distributions for classification often suffers from the following problems: a) data insufficiency implying overtrained or unreliable models b) large storage requirement c) large computational requirement and/or d) ML is not discriminating between classes. Sharing parameters across classes (or constraining the parameters) clearly tends to alleviate the first three problems. It this paper we show that in some cases it can also lead to better discrimination (as evidenced by reduced misclassification error). The parameters considered are the means and variances of the gaussians and linear transformations of the feature space (or equivalently the gaussian means). Some forms of sharing (either explicit or implicit via constraints) on the parameters are shown to lead to Linear Discrimination Analysis (a well-known result) while others (like diagonal, block-diagonal and factor analyzed covariances) are shown to lead to optimal feature spaces. The key idea is that in constrained ML modeling one may be able to better model the data after it is linearly transformed, perhaps in a class dependent fashion. If the constrains are invariant to linear transformations (ILT), then, the original feature space is as good as any to model the data. Results using optimal feature spaces for diagonal covariances is shown using the speech recognition problem as an example.
Enhancing GMM scores using SVM "hints"

This paper proposes a classification scheme that combines statistical models and support vector machines. It exploits the fact (observed in ) that GMM and SVM classifiers with roughly the same level of performance produce uncorrelated errors. We describe a novel scheme which employs an SVM classifier as an “advisor” to the GMM classifier in uncertain cases. The utility of the combined generative/discriminative approach is demonstrated on standard text-independent speaker verification and speaker identification tasks in matched and mismatched training and test conditions. Results indicate significant improvements in performance without much computational overhead.
A Suite of Simple Algorithms for Support Vector Machine Optimization

Recently Mangasarian and Musicant  suggested an interesting modification of the SVM objective with 2-norm loss, called the Lagrangian SVM (LSVM), that leads to a simple optimization algorithm. The LSVM algorithm is simple enough that it can applied even when the training size is large (millions of samples) and has been experimentally found to have generalization performance similar to standard SVMs . This paper takes inspiration from [1] and develops a suite of simple algorithms for SVM optimization. The algorithms use the special structure of the SVM QP problem a suggest a set of solutions. Algorithms are developed for both 1-norm and 2-norm loss functions as well as for bias-regularization and fixed bias. The algorithm developed here for the special LSVM problem is more efficient than the one in . In many applications the SVM QP problem is so large that the so-called kernel matrix cannot be stored efficiently. In such cases we suggest a novel lower-dimensional approximation of the kernel matrix. There are many criteria with respect to which these approximations can be obtained and we give a generic result on the influence of quality of the approximation of the kernel matrix on the quality of the solution to the original SVM optimization problem. A similar idea was proposed in Fine and Schienberg .
Robust confidence annotation and rejection for continuous speech recognition

We are looking for confidence scoring techniques that perform well on a broad variety of tasks. Our main focus is on word-level error rejection, but most results apply to other scenarios as well. A variation of the normalized cross entropy that is adapted to that purpose is introduced. It is successfully used to automatically select features and optimize the word-level confidence measure on several test sets. Sentence-level confidence geared toward the rejection of out-of-grammar utterances is also investigated. The combination of a word graph based technique and the acoustic score shows excellent performance across all the tasks we considered.
A hybrid GMM/SVM approach to speaker identification

Proposes a classification scheme that incorporates statistical models and support vector machines. A hybrid system which appropriately combines the advantages of both the generative and discriminant model paradigms is described and experimentally evaluated on a text-independent speaker recognition task in matched and mismatched training and test conditions. Our results prove that the combination is beneficial in terms of performance and practical in terms of computation. We report relative improvements of up to 25% reduction in identification error rate compared to the baseline statistical model.
Low-Resource Speech Recognition of 500-Word Vocabularies

We describe techniques for enhancing the accuracy, efficien cy and features of a low-resource, medium-vocabulary, gramma rbased speech recognition system. Among the issues and techniques we explore are front-end speech / silence detection t o reduce computational workload, the use of the Bayesian info rmation criterion ( BIC) to build smaller and better acoustic models, the minimization of finite state grammars, the use of hybrid maximum likelihood and discriminative models, and the automatic generation of baseforms from single new-word utt erances. We report WER figures throughout, as appropriate.
Multiple linear transforms

Heteroscedastic discriminant analysis (HDA) has been proposed as a replacement for linear discriminant analysis (LDA) in speech recognition systems that use mixtures of diagonal covariance Gaussians to model the data. Typically HDA and LDA involve a dimension reduction of the feature space. A specific version HDA that involves no dimension reduction; and is popularly known as maximum likelihood linear transform (MLLT) is often used on the feature space to give significant improvements in performance. MLLT approximately diagonalizes the class covariances, and in effect, tries to approximate the performance of a full-covariance-system. However, the performance of a full-covariance system could in some cases be much better than using MLLT-based diagonal covariance system. We propose the method of multiple linear transforms, that bridges this gap in performance, while maintaining the speed efficiency of a diagonal covariance system. This technique improves the performance of a diagonal covariance system, over what could be obtained from HDA or MLLT.
The IBM Personal Speech Assistant

We describe technology and experience with an experimental personal information manager, which interacts with the user primarily but not exclusively through speech recognition and synthesis. This device, which controls a client PDA, is known as the personal speech assistant (PSA). The PSA contains complete speech recognition, speech synthesis and dialog management systems. Packaged in a hand-sized enclosure, of size and physical design to mate with the popular Palm III personal digital assistant, the PSA includes its own battery, microphone, speaker, audio input and output amplifiers, processor and memory. The PSA supports speaker-independent English speech recognition using a 500-word vocabulary, and English speech synthesis on an arbitrary vocabulary. We survey the technical issues we encountered in building the hardware and software for this device, and the solutions we implemented, including audio system design, power and space budget, speech recognition in adverse acoustic environments with constrained processing resources, dialog management, appealing applications, and overall system architecture.
Eliminating inter-speaker variability prior to discriminant transforms

This paper shows the impact of speaker normalization techniques, such as vocal tract length normalization (VTLN) and speaker-adaptive training (SAT), prior to discriminant feature space transforms, such as LDA (linear discriminant analysis). We demonstrate that removing the inter-speaker variability by using speaker compensation methods results in improved discrimination as measured by the LDA eigenvalues and also in improved classification accuracy (as measured by the word error rate). Experimental results on the SPINE (speech in noisy environments) database indicate an improvement of up to 5% relative over the standard case where speaker adaptation (during testing and training) is applied after the LDA transform which is trained in a speaker independent manner. We conjecture that performing linear discriminant analysis in a canonical feature space (or speaker normalized space) is more effective than LDA in a speaker independent space because the eigenvectors carve a subspace of maximum intra-speaker phonetic separability whereas in the latter case this subspace is also defined by the inter-speaker variability. Indeed, we show that the more normalization is performed (first VTLN, then SAT), the higher the LDA eigenvalues become.
Robust speech recognition with multi-channel codebook dependent cepstral normalization (MCDCN)

We address the issue of speech recognition in the presence of interfering signals, in cases where the signals corrupting the speech are recorded in separate channels. We propose to combine a trivial form of filtering with MCDCN, a multi-channel version of codebook dependent cepstral normalization, where the cepstra of the noise are estimated from the reference signals. We report on recognition experiments in a car where the speech signal is corrupted by radio talks or CD music played by the car speakers. Our approach allows relative word error rate reductions in the range of 70-90% compared to a no-compensation baseline, at a relatively low computational cost.
Automatic generation and selection of multiple pronunciations for dynamic vocabularies

We present a scheme for the acoustic modeling of speech recognition applications requiring dynamic vocabularies. It applies especially to the acoustic modeling of out-of-vocabulary words which need to be added to a recognition lexicon based on the observation of a few (say one or two) speech utterances of these words. Standard approaches to this problem derive a single pronunciation from each speech utterance by combining acoustic and phone transition scores. In our scheme, multiple pronunciations are generated from each speech utterance of a word to enroll by varying the relative weights assigned to the acoustic and phone transition models. In our experiments, the use of these multiple baseforms dramatically outperforms the standard approach with a relative decrease of the word error rate ranging from 20% to 40% on all our test sets.
Large-Scale Support Vector Machine Training using Low-Dimensional Approximation of the Kernel Matrix

Training Support Vector Machines (regression and/or classification) involves solving a simply constrained quadratic programming problem that scales linearly with training set size. Unfortunately this makes solving large-scale problems with millions of training samples difficult if not impossible from both computational and storage considerations. A standard approach is to exploit the sparsity of the optimal solution (# of support vectors) as in decomposition/chunking or work with few variables at a time as in SMO. Yet another approach  is to exploit the low-rank nature of the kernel matrix. The applicability of this is limited clearly to those kernels that have a good low-rank approximation. This paper introduces the notion of low-dimensional approximation of the kernel matrix that naturally specializes to the methods in  for the low-rank case. Exact approximation results and approximate results based on computational considerations are given. The advantage of our scheme is that it directly translates to interior point methods for QP in . Our approximation results may be of relevance in interior point methods for solving LPs approximately. Also, the approximation results in this paper should be of interest in statistical modeling with Gaussian Mixtures as well as Gaussian Process classification and regression.
Gaussianization

High dimensional data modeling is difficult mainly because the so-called "curse of dimensionality". We propose a technique called "Gaussianization" for high dimensional density estimation, which alleviates the curse of dimensionality by exploiting the independence structures in the data. Gaussianization is motivated from recent developments in the statistics literature: projection pursuit, independent component analysis and Gaussian mixture models with semi-tied covariances. We propose an iterative Gaussianization procedure which converges weakly: at each iteration, the data is first transformed to the least dependent coordinates and then each coordinate is marginally Gaussianized by univariate techniques. Gaussianization offers density estimation sharper than traditional kernel methods and radial basis function methods. Gaussianization can be viewed as efficient solution of nonlinear independent component analysis and high dimensional projection pursuit.
Transcription of broadcast news with a time constraint: IBM's 10xRT HUB4 system

We describe a system which automatically transcribes broadcast news in less than 10 times real-time. We detail the system architecture of this system, which was used by IBM in the 1999 HUB4 10xRT evaluation, and show that the performance of this system is over 20 percent more accurate at the same speed than the system we used in the 1998 evaluation. Furthermore, we have closed the gap in word recognition accuracy between an unlimited resource system and this which runs in under 10 times real time from 45 percent to 14 percent.
Transformation enhanced multi-grained modeling for text-independent speaker recognition

We describe our formulation of transformation enhanced data modeling used to develop a multi-grained data analysis approach to text independent speaker recognition. The broad goal is to address difficulties caused by sparse training and test data. First, our development of maximum likelihood transformation based recognition with diagonally constrained Gaussian mixture models is detailed. We give results to show its robustness to decreasing training data. Then using the these models as building blocks, a multigrained model structure is developed. For this, the training data must be labeled, e.g. with an HMM based phone labeler. A graduated phone class structure is then used to train the speaker model at various levels of detail. This structure is a tree with the root node containing all the phones. Subsequent levels partition the phones into increasingly finer grained linguistic classes. We demonstrate the effectiveness of the modeling with identification and verification experiments.
IBM ’ s 10 x Real-time Broadcast News Transcription System Used in the 1999 Hub 4 Evaluation

We describe the system used by IBM in the 1999 HUB4 Evaluation under the 10 times real-time constraint. We detail the s ystem architecture and show that the performance of this system is over 20 percent more accurate at the same speed than the system used i n th 1998  Furthermore, we have closed the gap betwee n our unlimited resource system and our 10 times real time system f ro 45 percent to 14 percent.
Maximum likelihood discriminant feature spaces

Linear discriminant analysis (LDA) is known to be inappropriate for the case of classes with unequal sample covariances. There has been an interest in generalizing LDA to heteroscedastic discriminant analysis (HDA) by removing the equal within-class covariance constraint. This paper presents a new approach to HDA by defining an objective function which maximizes the class discrimination in the projected subspace while ignoring the rejected dimensions. Moreover, we investigate the link between discrimination and the likelihood of the projected samples and show that HDA can be viewed as a constrained ML projection for a full covariance Gaussian model, the constraint being given by the maximization of the projected between-class scatter volume. It is shown that, under diagonal covariance Gaussian modeling constraints, applying a diagonalizing linear transformation (MLLT) to the HDA space results in increased classification accuracy even though HDA alone actually degrades the recognition performance. Experiments performed on the Switchboard and Voicemail databases show a 10%-13% relative improvement in the word error rate over standard cepstral processing.
Model selection in acoustic modeling

Recently several classes of models have been suggested for use in continuous density HMMs for speech recognition. This paper proposes to choose both the model type and model size (number of parameters) by optimizing the Bayesian information criterion. Speci cally we apply this to Gaussian mixture density estimation to determine both the number of Gaussians and the covariance structure of each Gaussian, and decision tree clustering of HMM states. A numerical algorithm similar to the EM algorithm for mixture density estimation is proposed for optimizing BIC.
Improved speaker segmentation and segments clustering using the bayesian information criterion

Detection of speaker, channel and environment changes in a continuous audio stream is important in various applications (e.g., broadcast news, meetings/teleconferences etc.). Standard schemes for segmentation use a classi er and hence do not generalize to unseen speaker / channel / environments. Recently S.Chen introduced new segmentation and clustering algorithms, using the so-called BIC. This paper presents more accurate and more e cient variants of the BIC scheme for segmentation and clustering. Speci cally, the new algorithms improve the speed and accuracy of segmentation and clustering and allow for a real-time implementation of simultaneous transcription, segmentation and speaker tracking.
Recent improvements to IBM's speech recognition system for automatic transcription of broadcast news

We describe extensions and improvements to IBM's system for automatic transcription of broadcast news. The speech recognizer uses a total of 160 hours of acoustic training data, 80 hours more than for the system described in Chen et al. (1998). In addition to improvements obtained in 1997 we made a number of changes and algorithmic enhancements. Among these were changing the acoustic vocabulary, reducing the number of phonemes, insertion of short pauses, mixture models consisting of non-Gaussian components, pronunciation networks, factor analysis (FACILT) and Bayesian information criteria (BIC) applied to choosing the number of components in a Gaussian mixture model. The models were combined in a single system using NIST's script voting machine known as rover (Fiscus 1997).
The IBM LVCSR System Used for 1998 Mandarin Broadcast News Transcription Evaluation

This paper presents the technologies implemented in the IBM's Large Vocabulary Continuous Speech Recognition(LVCSR) system which was designed for 1998 Mandarin broadcast news transcription evaluation task. Compared with the 1997 system, it focuses on acoustic improvements by implementing several new schemes such as LDA and MLLT transformation matrix, BIC model selection criterion, SAT and CAT models. In addition, new language model components and new vocabulary were built. Some other schemes which were tried we also described.
Factor analysis invariant to linear transformations of data

Modeling data with Gaussian distributions is an important statistical problem. To obtain robust models one imposes constraints the means and covariances of these distributions [6, 4, 10, 8]. Constrained ML modeling implies the existence of optimal feature spaces where the constraints are more valid [2, 3]. This paper introduces one such constrained ML modeling technique called factor analysis invariant to linear transformations (FACILT) which is essentially factor analysis in optimal feature spaces. FACILT is a generalization of several existing methods for modeling covariances. This paper presents an EM algorithm for FACILT modeling.
IBM's LVCSR system for transcription of broadcast news used in the 1997 hub4 english evaluation

This paper describes IBM’s large vocabulary continuous speech recognition (LVCSR) system used in the 1997 Hub4 English evaluation. It focusses on extensions and improvements to the system used in the 1996 evaluation. The recognizer uses an additional 35 hours of training data over the one used in the 1996 Hub4 evaluation . It includes a number of new features: optimal feature space for acoustic modeling (in training and/or testing), filler-word modeling, Bayesian Information Criterion (BIC) based segmentation and segment clustering, an improved implementation of iterative MLLR, variance adaptation, and 4-gram language models. Results using the 1996 and 1997 DARPA Hub4 evaluation data sets are presented.
Techniques for capturing temporal variations in speech signals with fixed-rate processing

Fixed-rate feature extraction which is used in most current speech recognizers is equivalent to sampling the feature trajectories at a uniform rate. Often this sampling rate is well below the Nyquist rate and thus leads to distortions in the sampled feature stream due to aliasing. In this paper we explore various techniques, ranging from simple cepstral and spectral smoothing to ltering and data-driven dimensionality expansion using Linear Discriminant Analysis (LDA), to counter aliasing and the variable rate nature of information in speech signals. Smoothing in the spectral domain results in a reduction in the variance of the short term spectral estimates which directly translates to reduction in the variances of the Gaussians in the acoustic models. With these techniques we obtain modest improvements, both in word error rate and robustness to noise, on large vocabulary speech recognition tasks.
Transcription of broadcast news-some recent improvements to IBM's LVCSR system

This paper describes extensions and improvements to IBM's large vocabulary continuous speech recognition (LVCSR) system for transcription of broadcast news. The recognizer uses an additional 35 hours of training data over the one used in the 1996 Hub4 evaluation. It includes a number of new features: optimal feature space for acoustic modeling (in training and/or testing), filler-word modeling, Bayesian information criterion (BIC) based segment clustering, an improved implementation of iterative MLLR and 4-gram language models. Results using the 1996 DARPA Hub4 evaluation data set are presented.
Maximum likelihood modeling with Gaussian distributions for classification

Maximum likelihood (ML) modeling of multiclass data for classification often suffers from the following problems: (a) data insufficiency implying overtrained or unreliable models, (b) large storage requirement, (c) large computational requirement and/or (d) the ML is not discriminating between classes. Sharing parameters across classes (or constraining the parameters) clearly tends to alleviate the first three problems. We show that in some cases it can also lead to better discrimination (as evidenced by reduced misclassification error). The parameters considered are the means and variances of the Gaussians and linear transformations of the feature space (or equivalently the Gaussian means). Some constraints on the parameters are shown to lead to linear discrimination analysis (a well-known result) while others are shown to lead to optimal feature spaces (a relatively new result). Applications of some of these ideas to the speech recognition problem are also given.