nils-herrmann/clean_scirepeval huggingface dataset:
A Submodular-supermodular Procedure with Applications to Discriminative Structure Learning

In this paper, we present an algorithm for minimizing the difference between two submodular functions using a variational framework which is based on (an extension of) the concave-convex procedure [17]. Because several commonly used metrics in machine learning, like mutual information and conditional mutual information, are submodular, the problem of minimizing the difference of two submodular problems arises naturally in many machine learning applications. Two such applications are learning discriminatively structured graphical models and feature selection under computational complexity constraints. A commonly used metric for measuring discriminative capacity is the EAR measure which is the difference between two conditional mutual information terms. Feature selection taking complexity considerations into account also fall into this framework because both the information that a set of features provide and the cost of computing and using the features can be modeled as submodular functions. This problem is NP-hard, and we give a polynomial time heuristic for it. We also present results on synthetic data to show that classifiers based on discriminative graphical models using this algorithm can significantly outperform classifiers based on generative graphical models.
Focused state transition information in ASR

We present speech recognition graphical models that use "focused evidence" to directly influence word and state transition probabilities in an explicit graphical-model representation of a speech recognition system. Standard delta and double delta features are used to detect loci of rapid change in the speech stream, and this information is applied directly to transition variables in a graphical model. Five different models are evaluated, and results are given on the highly mismatched training/testing condition tasks in Aurora 3.0. The best of these models gives an average 8% reduction in word error rate over baseline, significant at the 0.05 level
Energy and loudness for speed control in the vocal joystick

We propose and describe several methods for using speech power as an estimate of intentional loudness, and a mapping from this loudness estimate to a continuous control. This is performed in the context of a novel voice-based human-computer interface designed to enable individuals with motor impairments to use vocal tract parameters for both discrete and continuous control tasks. The interface uses vocal gestures to control continuous movement and discrete sounds for other events. We conduct a user preference survey to gauge user reaction to the various methods in a mouse cursor control context. We find that loudness is an effective mechanism to control mouse cursor movement speed when mapping vocalic gestures to spatial position
Focused word segmentation for ASR

We propose a new set of features based on the temporal statistics of the spectral entropy of speech. We show why these features make good inputs for a speech detector. Moreover, we propose a back-end that uses the evidence from the above features in a ‘focused’ manner. Subsequently, by means of recognition experiments we show that using the above back-end leads to significant performance improvements, but merely appending the features to the standard feature vector does not improve performance. We also report a 10% average improvement in word error rate over our baseline for the highly mis-matched case in the Aurora3.0 corpus.
Genetic triangulation of graphical models for speech and language processing

Graphical models are an increasingly popular approach for speech and language processing. As researchers design ever more complex models it becomes crucial to find triangulations that make inference problems tractable. This paper presents a genetic algorithm for triangulation search that is well-suited for speech and language graphical models. It is unique in two ways: First, it can find triangulations appropriate for graphs with a mix of stochastic and deterministic dependencies. Second, the search is guided by optimizing the inference speed (CPU runtime) on real data. We show results on 10 real-world speech and language graphs and demonstrate inference speed-ups over standard triangulation methods.
The Vocal Joystick Demo at UIST 05 : A Voice-Based Human-Computer Interface

We will demonstrate a novel voice-based human-computer interface we call the Vocal Joystick (VJ), designed to enable individuals with motor impairments to use vocal parameters for both discrete and continuous control tasks. Since discrete spoken commands are not ideally suited to such tasks, our methodology exploits a large set of continuous acoustic-phonetic parameters like pitch, loudness, vowel quality, etc. Their selection is optimized with respect to automatic recognizability, communication bandwidth, learnability, suitability, and ease of use. These parameters are extracted continually in real time, transformed via adaptation and acceleration, and converted into continuous control signals. Our UIST’2005 demo will allow the user to try out the vocal joystick on arbitrary web-browsing tasks, VJ-enabled games, and other VJ-based applications.
SVitchboard 1 : Small Vocabulary Ta

We present a conversational telephone speech data set designed to support research on novel acoustic models. Small vocabulary tasks from 10 words up to 500 words are defined using subsets of the Switchboard-1 corpus; each task has a completely closed vocabulary (an OOV rate of 0%). We justify the need for these tasks, describe the algorithm for selecting them from a large corpus, give a statistical analysis of the data and present baseline whole-word hidden Markov model recognition results. The goal of the paper is to define a common data set and to encourage other researchers to use it.
The Vocal Joystick: A Voice-Based Human-Computer Interface for Individuals with Motor Impairments

We present a novel voice-based human-computer interface designed to enable individuals with motor impairments to use vocal parameters for continuous control tasks. Since discrete spoken commands are ill-suited to such tasks, our interface exploits a large set of continuous acoustic-phonetic parameters like pitch, loudness, vowel quality, etc. Their selection is optimized with respect to automatic recognizability, communication bandwidth, learnability, suitability, and ease of use. Parameters are extracted in real time, transformed via adaptation and acceleration, and converted into continuous control signals. This paper describes the basic engine, prototype applications (in particular, voice-based web browsing and a controlled trajectory-following task), and initial user studies confirming the feasibility of this technology.
Part-of-Speech Tagging using Virtual Evidence and Negative Training

We present a part-of-speech tagger which introduces two new concepts: virtual evidence in the form of an "observed child" node, and negative training data to learn the conditional probabilities for the observed child. Associated with each word is a flexible feature-set which can include binary flags, neighboring words, etc. The conditional probability of Tag given Word + Features is implemented using a factored language-model with back-off to avoid data sparsity problems. This model remains within the framework of Dynamic Bayesian Networks (DBNs) and is conditionally-structured, but resolves the label bias problem inherent in the conditional Markov model (CMM).
A graphical model for formant tracking

We present a novel approach to estimating the first two formants (F1 and F2) of a speech signal using graphical models. Using a graph that takes advantage of less commonly used features of Bayesian networks, both v-structures and soft evidence, the model presented here shows that it can learn to perform reasonably without large amounts of training data, even with minimal processing on the initial signal. It far outperforms a factorial HMM using the same assumptions and suggests that with further refinement the model may produce high quality formant tracks.
DBN-based multi-stream models for Mandarin toneme recognition

A toneme in Mandarin Chinese is a tonal phone which consists of a base phone (main vowel) and a tone. To capture both, most recognition systems use two feature streams: the standard MFCC for the base phones, and pitch features for the tones. In this paper we propose the use of dynamic Bayesian networks for modeling the two streams in toneme recognition. We used the Graphical Model Toolkit to build and compare three different models: a standard HMM with concatenated features, and synchronous and asynchronous multi-stream systems. Stream-level model parameter tying is also exploited. The toneme recognition results show significant improvements by using the multi-stream models.
Feature pruning for low-power ASR systems in clean and noisy environments

Likelihood evaluation can substantially affect the total computational load for continuous hidden Markov model (HMM)-based speech-recognition systems with small vocabularies. This letter presents feature pruning , a simple yet effective technique to reduce computation and, hence, power consumption of likelihood evaluation. Our technique, under certain conditions, only evaluates the likelihoods of a fraction of feature elements and approximates those of the remaining (pruned) ones by a simple function. The order in which feature elements are evaluated is obtained by a data-driven approach to minimize computation. With this order, feature pruning can speed up the likelihood evaluation by a factor of 1.3-1.8 and reduce its power consumption by 27%-43% for various recognition tasks, including those in noisy environments.
Maximum margin learning and adaptation of MLP classifiers

Conventional MLP classifiers used in phonetic recognition and speech recognition may encounter local minima during training, and they often lack an intuitive and flexible adaptation approach. This paper presents a hybrid MLP-SVM classifier and its associated adaptation strategy, where the last layer of a conventional MLP is learned and adapted in the maximum separation margin sense. This structure also provides a support vector based adaptation mechanism which better interpolates between a speaker-independent model and speakerdependent adaptation data. Preliminary experiments on vowel classification have shown promising results for both MLP learning and adaptation problems.
Graphical model architectures for speech recognition

This article discusses the foundations of the use of graphical models for speech recognition as presented in J. R. Deller et al. (1993), X. D. Huang et al. (2001), F. Jelinek (19970, L. R. Rabiner and B. -H. Juang (1993) and S. Young et al. (1990) giving detailed accounts of some of the more successful cases. Our discussion employs dynamic Bayesian networks (DBNs) and a DBN extension using the Graphical Model Toolkit's (GMTK's) basic template, a dynamic graphical model representation that is more suitable for speech and language systems. While this article concentrates on speech recognition, it should be noted that many of the ideas presented here are also applicable to natural language processing and general time-series analysis.
Discriminative versus generative parameter and structure learning of Bayesian network classifiers

In this paper, we compare both discriminative and generative parameter learning on both discriminatively and generatively structured Bayesian network classifiers. We use either maximum likelihood (ML) or conditional maximum likelihood (CL) to optimize network parameters. For structure learning, we use either conditional mutual information (CMI), the explaining away residual (EAR), or the classification rate (CR) as objective functions. Experiments with the naive Bayes classifier (NB), the tree augmented naive Bayes classifier (TAN), and the Bayesian multinet have been performed on 25 data sets from the UCI repository (Merz et al., 1997) and from (Kohavi & John, 1997). Our empirical study suggests that discriminative structures learnt using CR produces the most accurate classifiers on almost half the data sets. This approach is feasible, however, only for rather small problems since it is computationally expensive. Discriminative parameter learning produces on average a better classifier than ML parameter learning.
A Dynamic Bayesian Framework to Model Context and Memory in Edit Distance Learning: An Application to Pronunciation Classification

Sitting at the intersection between statistics and machine learning, Dynamic Bayesian Networks have been applied with much success in many domains, such as speech recognition, vision, and computational biology. While Natural Language Processing increasingly relies on statistical methods, we think they have yet to use Graphical Models to their full potential. In this paper, we report on experiments in learning edit distance costs using Dynamic Bayesian Networks and present results on a pronunciation classification task. By exploiting the ability within the DBN framework to rapidly explore a large model space, we obtain a 40% reduction in error rate compared to a previous transducer-based method of learning edit distance.
Q-Clustering

We show that Queyranne's algorithm for minimizing symmetric submodular functions can be used for clustering with a variety of different objective functions. Two specific criteria that we consider in this paper are the single linkage and the minimum description length criteria. The first criterion tries to maximize the minimum distance between elements of different clusters, and is inherently "discriminative". It is known that optimal clusterings into k clusters for any given k in polynomial time for this criterion can be computed. The second criterion seeks to minimize the description length of the clusters given a probabilistic generative model. We show that the optimal partitioning into 2 clusters, and approximate partitioning (guaranteed to be within a factor of 2 of the the optimal) for more clusters can be computed. To the best of our knowledge, this is the first time that a tractable algorithm for finding the optimal clustering with respect to the MDL criterion for 2 clusters has been given. Besides the optimality result for the MDL criterion, the chief contribution of this paper is to show that the same algorithm can be used to optimize a broad class of criteria, and hence can be used for many application specific criterion for which efficient algorithm are not known.
Custom arithmetic for high-speed, low-resource ASR systems

With the skyrocketing popularity of mobile devices, new processing methods, tailored for low-resource systems, have become necessary. We propose the use of custom arithmetic logic tailored to a specific application. In a system with all parameters quantized to low precision, such arithmetic can be implemented through a set of small, fast table lookups. We present here a framework for the design of such a system architecture, and several heuristic algorithms to optimize system performance. In addition, we apply our techniques to an automatic speech recognition (ASR) application. Our simulations on various architectures show that on most modern processor designs, we can expect a cycle-count speedup of at least 3 times while requiring a total of only 59 kB of ROMs to hold the lookup tables.
Noise robustness in automatic speech recognition

The issue of noise robustness in automatic speech recognition is of practical importance and largely unsolved. In this thesis, this problem is tackled from both perspectives of front-end speech features and back-end speech models. For the front end, a feature processing technique consisting of mean subtraction, variance normalization and ARMA filtering is investigated. Mathematical analyses are carried out for the distortion of speech features in the presence of additive and convolutional noises. Extensive experiments are conducted to see how to best use this front-end technique. It is experimentally verified to be extremely effective for the noisy-digit databases of Aurora. This performance gain is achieved without increasing the model parameters and computational cost. For the back end, a novel random variable called a feature selector is introduced into speech models to dynamically select a robust component feature to score, ignoring the others. The values of the feature selectors are based on either the energy or the spectral entropy of the signal. This back-end technique does not lead to significant performance gain with the feature streams investigated in this work, MFCCs and post-processed MFCCs. Yet it is a novel scheme of integrating multiple information sources.
Elimination is Not Enough: Non-Minimal Triangulations for Graphical Models

As is well known, exact probabilistic graphical inference requires a triangulated graph. Different triangulations can make exponential differences in complexity, but since nding the optimum is intractable, a wide variety of heuristics have been proposed, most involving a vertex elimination ordering. Elimination always yields a triangulated graph, can produce all edge minimal triangulations, and can also produce triangulations having the smallest maximal clique size. In this paper, we show that there are cases of practical importance where the optimal triangulation is unobtainable with elimination. Specically , we show that real-world models with deterministic dependencies exist where the best elimination-based triangulation can have an unboundedly larger state space than the optimal triangulation. We present new methods that, unlike elimination, can generate optimal state space triangulations for such models, and give results for both real and randomly generated graphs. We also give an algorithm and correctness proof for determining if a triangulation can be obtained via elimination, and give a new proof that the decision problem associated with nding optimal state space triangulations is NP-complete.
Multi-Speaker Language Modeling

In conventional language modeling, the words from only one speaker at a time are represented, even for conversational tasks such as meetings and telephone calls. In a conversational or meeting setting, however, speakers can have significant influence on each other. To recover such un-modeled inter-speaker information, we introduce an approach for conversational language modeling that considers words from other speakers when predicting words from the current one. By augmenting a normal trigram context, our new multi-speaker language model (MSLM) improves on both Switchboard and ICSI Meeting Recorder corpora. Using an MSLM and a conditional mutual information based word clustering algorithm, we achieve a 8.9% perplexity reduction on Switchboard and a 12.2% reduction on the ICSI Meeting Recorder data.
Learning Graphical Models over partial k-trees

We show that the class of strongly connected graphical models with tree-width at most k can be properly efficiently PAC-learnt with respect to the Kullback-Leibler Divergence. Previous approaches to this problem, such as those of Chow (), and Hoffgen () have shown that this class is PAC-learnable (though not necessarily efficiently unless k = 1) by reducing to a NP-complete combinatorial optimization problem. Unless P=NP, these approaches will take exponential amounts of time. Our approach differs significantly from these, in that it first attempts to find approximate conditional independencies by solving (polynomially many) submodular optimization problems, and then using a dynamic programming formulation to combine the approximate conditional independence information to derive a graphical model with underlying graph of the tree-width specified. This gives us an efficient (polynomial time) PAC-learning algorithm which requires only polynomial number of samples of the true distribution, and only polynomial running time.
Graphical models and automatic speech recognition

Graphical models provide a promising paradigm to study both existing and novel techniques for automatic speech recognition. This paper first provides a brief overview of graphical models and their uses as statistical models. It is then shown that the statistical assumptions behind many pattern recognition techniques commonly used as part of a speech recognition system can be described by a graph – this includes Gaussian distributions, mixture models, decision trees, factor analysis, principle component analysis, linear discriminant analysis, and hidden Markov models. Moreover, this paper shows that many advanced models for speech recognition and language processing can also be simply described by a graph, including many at the acoustic-, pronunciation-, and language-modeling levels. A number of speech recognition techniques born directly out of the graphical-models paradigm are also surveyed. Additionally, this paper includes a novel graphical analysis regarding why derivative (or delta) features improve hidden Markov model-based speech recognition by improving structural discriminability. It also includes an example where a graph can be used to represent language model smoothing constraints. As will be seen, the space of models describable by a graph is quite large. A thorough exploration of this space should yield techniques that ultimately will supersede the hidden Markov model.
Blind MVA Speech Feature Processing on Aurora 2.0

This paper is focused on the MVA (mean subtraction, variance normalization, and ARMA filtering) feature postprocessing scheme for noise-robust automatic speech recognition. MVA has shown great success in the past on the Aurora 2.0 and 3.0 corpora. To test its generality, in this work MVA is blindly applied to many different acoustic feature extraction methods, and is evaluated using the Aurora 2.0 corpus. Specifically, we apply MVA post-processing to feature extraction techniques including: MFCC, LPC, PLP, RASTA, Tandem, Modulation-filtered Spectrogram and Modulation Cross-CorreloGram. We find that while effectiveness depends on the extraction method used, the majority of features benefit significantly from MVA. We conclude with a brief analysis.
Object class recognition using images of abstract regions

With the advent of many large image databases, both commercial and personal, content-based image retrieval has become an important research area. While most early efforts retrieved images based on appearance, it is now recognized that most users want to retrieve images based on the objects present in them. This paper addresses the challenging task of recognizing common objects in color photographic images. We represent images as sets of feature vectors of multiple types of abstract regions, which come from various segmentation processes. We model each abstract region as a mixture of Gaussian distributions over its feature space. We have developed a new semi-supervised version of the EM algorithm for learning the distributions of the object classes. We use supervisory information to tell the procedure the set of objects that exist in each training image, but we do not use any such supervisory information about where (ie. in which regions) the objects are located in the images. Instead, we rely on our EM-like algorithm to break the symmetry in an initial solution that is estimated with error. Experiments are conducted on a set of 860 images to show the efficacy of our approach.