nils-herrmann/clean_scirepeval huggingface dataset:
Speaker adaptation experiments using nonstationary-state hidden Markov models: a MAP approach

We report our recent work on applications of the MAP approach to estimating the time-varying polynomial Gaussian mean functions in the nonstationary-state or trended HMM. Assuming uncorrelatedness among the polynomial coefficients in the trended HMM, we have obtained analytical results for the MAP estimates of the time-varying mean and precision parameters. We have implemented a speech recognizer based on these results in speaker adaptation experiments using the T146 corpora. Experimental results show that the trended HMM always outperforms the standard, stationary-state HMM and that adaptation of polynomial coefficients only is better than adapting both polynomial coefficients and precision matrices when fewer than four adaptation tokens are used.
Use of generalized dynamic feature parameters for speech recognition

In this study, a new hidden Markov model that integrates generalized dynamic feature parameters into the model structure is developed and evaluated using maximum-likelihood (ML) and minimum-classification-error (MCE) pattern recognition approaches. In addition to the motivation of direct minimization of error rate, the MCE approach automatically eliminates the necessity of artificial constraints, which were essential for the model formulation based on the ML approach, on the weighting functions in the definition of the generalized dynamic parameters. We design the loss function for minimizing error rate specifically for the new model, and derive an analytical form of the gradient of the loss function that enables the implementation of the MCE approach. The convergence property of the training procedure based on the MCE approach is investigated, and the experimental results from a standard TIMIT phonetic classification task demonstrate a 13.4% error rate reduction compared with the ML approach.
A Dynamic, Feature-Based Approach Modeling and Recognition to Speech

An overview of a statistical paradigm for speech recog- nition is given where phonetic and phonological knowledge sources are seaimlessly integrated into the structure of a speech model. A unifying computational formalism is outlined in which the sub-models for the dis- crete, feature-based phonological and the continuous, dynamic phonetic processes in human speech production are computationally interfaced, enabling global optimization of the model parameter sets that economi- cally characterize distinct sources of speech variabilities. The formalism is founded on a rigorous mathematical basis, and is developed to aim at overcoming key limitations of current speech recognition technology.
A dynamic, feature-based approach to speech modeling and recognition

An overview of a statistical paradigm for speech recognition is given where phonetic and phonological knowledge sources are seamlessly integrated into the structure of a speech model. A unifying computational formalism is outlined in which the sub-models for the discrete, feature-based phonological and the continuous, dynamic phonetic processes in human speech production are computationally interfaced, enabling global optimization of the model parameter sets that economically characterize distinct sources of speech variabilities. The formalism is founded on a rigorous mathematical basis, and is developed to aim at overcoming key limitations of current speech recognition technology.
Integrated-multilingual speech recognition using universal phonological features in a functional speech production model

An outline and general design of an integrated-multilingual speech recognizer is presented, focusing on its key novelty of cross-language portability. This recognizer extends the one described by Deng and Sun  1994) in that the overlapping features designed originally for American English are improved, generalized, and need only a slight expansion to cover Mandarin/Cantonese Chinese and Canadian French. It also enhances the recognizer of Deng and Sameti (see  1996) in that the object of dynamic modeling is moved from the observable acoustic domain to the hidden production-affiliated variables defined in the task-dynamic model of speech production. The major components of the recognizer and the related training and recognition algorithms are described.
HMM-based speech recognition using state-dependent, discriminatively derived transforms on mel-warped DFT features

In the study reported in this paper, we investigate interactions of front-end feature extraction and back-end classification techniques in hidden Markov model-based (HMM-based) speech recognition. The proposed model focuses on dimensionality reduction of the mel-warped discrete Fourier transform (DFT) feature space subject to maximal preservation of speech classification information, and aims at finding an optimal linear transformation on the mel-warped DFT according to the minimum classification error (MCE) criterion. This linear transformation, along with the HMM parameters, are automatically trained using the gradient descent method to minimize a measure of overall empirical error counts. A further generalization of the model allows integration of the discriminatively derived state-dependent transformation with the construction of dynamic feature parameters. Experimental results show that state-dependent transformation on mel-warped DFT features is superior in performance to the mel-frequency cepstral coefficients (MFCC's). An error rate reduction of 15% is obtained on a standard 39-class TIMIT phone classification task, in comparison with the conventional MCE-trained HMM using MFCC's that have not been subject to optimization during training.
Hierarchical partition of the articulatory state space for overlapping-feature based speech recognition

Describes our recent work on improving an overlapping articulatory feature (sub-phonemic) based speech recognizer with robustness to the requirement of training data. A new decision-tree algorithm is developed and applied to the recognizer design which results in hierarchical partitioning of the articulatory state space. The articulatory states associated with common acoustic correlates (a phenomenon caused by the many-to-one articulation-to-acoustics mapping that is well-known in speech production) are automatically clustered by the decision-tree algorithm. This enables effective prediction of the unseen articulatory states in the training, thereby increasing the recognizer's robustness. Some preliminary experimental results are provided.
Modeling context-dependent phonetic units in a continuous speech recognition system for Mandarin Chinese

Studies the problem of phonetic modeling for continuous Mandarin speech recognition by providing a systematic performance comparison for systems based on following primitive speech units: syllables, demi-syllables (initial and final), context-independent phones, left-or-right context-dependent phones (diphones) and left-and-right context-dependent phones (triphones). In our speaker-dependent continuous speech recognition experiments, a generalized triphone system has achieved the best performance of all. Our best system is in contrast to most other Mandarin speech recognition systems, which have been based on demi-syllable units.
Simulation of disordered speech using a frequency-domain vocal tract model

Addresses the issue of how the perception of disorder in selected types of speech disorders may be correlated with abnormal articulatory structure and with related acoustic properties. As a first step towards this end, we have developed an articulatory synthesiser based on a frequency-domain simulation of vocal-tract wave propagation. The synthesiser has been implemented by three numerical methods-Runge-Kutta, ABCD matrix and finite differences-which provide frequency-domain solutions to the transmission-line equation characterizing a lossy vocal tract. The synthesiser is applied in preliminary experiments where the its outputs are used to match samples from a corpus of steady-state speech sound, obtained from a dysarthric speaker, uttered in the /hV/ context.
Non-Stationary Hidden Markov Models for Speech Recognition

The standard hidden Markov models (HMM) assume local or state-conditioned stationarity of the signals being modeled. In this article, we present some recent development in generalizing the standard HMM to incorporate the local dynamic patterns as well as the global non-stationarity for speech signal modeling. The major component of the proposed non-stationary HMMs is the parametric regression models for individual HMM states. The regression functions are intended for characterizing the dynamic movements of the signals within a HMM state. Both the EM algorithm (or Baum-Welch algorithm) and the segmental K-means algorithms are generalized to accommodate the complex state duration information needed for the estimation of regression parameters. To allow for the flexibility of linear time warping in individual HMM states, an efficient algorithm is developed with the use of token-dependent auxiliary parameters. Although the auxiliary parameters are of no interest in themselves for modeling speech sound patterns, they provide an intermediate tool for achieving maximal accuracy in estimating the parameters of the regression models.
HMM-based speech recognition using state-dependent, linear transforms on Mel-warped DFT features

We investigate the interactions of front-end feature extraction and back-end classification techniques in HMM based speech recognizer. This work concentrates on finding the optimal linear transformation of Mel-warped short-time DFT information according to the minimum classification error criterion. These transformations, along with the HMM parameters, are automatically trained using the gradient descent method to minimize a measure of overall empirical error count. The discriminatively derived state-dependent transformations on the DFT data are then combined with their first time derivatives to produce a basic feature set. Experimental results show that Mel-warped DFT features, subject to appropriate transformation in a state-dependent manner, are more effective than the Mel-frequency cepstral coefficients that have dominated current speech recognition technology. The best error rate reduction of 9% is obtained using the new model, tested on a TIMIT phone classification task, relative to conventional HMM.
Transitional speech units and their representation by regressive Markov states: applications to speech recognition

Transitional speech units for American English are proposed and constructed via assimilation of active articulatory features. The effectiveness of a feature-based system exploiting the transitional speech units is demonstrated in evaluation experiments, where the new system with use of quadratic regressive states is shown to achieve error rate reduction of 21% compared with the system using only static subphonemic units.
The trended HMM with discriminative training for phonetic classification

The authors extend the maximum likelihood (ML) training algorithm to the minimum classification error (MCE) training algorithm for optimal estimation of the state-dependent polynomial coefficients in the trended HMM. The problem of automatic speech recognition is viewed as a discriminative dynamic data-fitting problem, where relative (not absolute) closeness in fitting an array of dynamic speech models to the unknown speech data sequence provides the recognition decision. In this view, the properties of the MCE formulation for training the trended HMM are analyzed by fitting raw speech data using MCE-trained trended HMMs, contrasting the poor discriminative fitting using the ML-trained models. Comparisons between the phonetic classification as well as data-fitting results obtained with ML and with MCE training algorithms demonstrate the effectiveness of the discriminatively trained trended HMMs.
Optimal filtering and smoothing for speech recognition using a stochastic target model

Presents a stochastic target model of speech production, where articulator motion in the vocal tract is represented by the state of a Markov-modulated linear dynamical system, driven by a piecewise-deterministic control trajectory and observed through a non-linear function representing the articulatory-acoustic mapping. Optimal filtering and smoothing algorithms for estimating the hidden states of the model from acoustic measurements are derived using a measure-change technique and require the solution of recursive integral equations. A sub-optimal approximation is developed and illustrated using examples taken from real speech.
Use of generalized dynamic feature parameters for speech recognition: maximum likelihood and minimum classification error approaches

In this study we implemented a speech recognizer based on the integrated view, proposed first by Deng (see IEEE Signal Processing Letters, vol.1, no.4, p.66-69, 1994), on the speech preprocessing and speech modeling problems in the recognizer design. The integrated model we developed generalizes the conventional, currently widely used delta-parameter technique, which has been confined strictly to the preprocessing domain only, in two significant ways. First, the new model contains state-dependent weighting functions responsible for transforming static speech features into the dynamic ones in a slowly time-varying manner. Second, novel maximum-likelihood and minimum-classification-error based learning algorithms are developed for the model that allows joint optimization of the state-dependent weighting functions and the remaining conventional HMM parameters. The experimental results obtained from a standard TIMIT phonetic classification task provide preliminary evidence for the effectiveness of our new, general approaches to the use of the dynamic characteristics of speech spectra.
Analysis of acoustic-phonetic variations in fluent speech using TIMIT

We propose a hierarchically structured analysis of variance (ANOVA) method to analyze, in a quantitative manner, the contributions of various identifiable factors to the overall acoustic variability exhibited in fluent speech data of TIMIT processed in the form of mel-frequency cepstral coefficients. The results of the analysis show that the greatest acoustic variability in TIMIT data is explained by the difference among distinct phonetic labels in TIMIT, followed by the phonetic context difference given a fixed phonetic label. The variability among sequential sub-segments within each TIMIT-defined phonetic segment is found to be significantly greater than the gender, dialect region, and speaker factors. Our results serve to provide useful insights to the understanding of the roles of various components of speech recognizers in contributing to the ultimate speech recognition performance.
Improved speech modeling and recognition using multi-dimensional articulatory states as primitive speech units

We provide a formal description of a speech recognizer designed on the basis of elaborate articulatory timing that a asynchronous across the multiple articulatory-feature dimensions. Three improved critical components of the recognizer are described in detail. Evaluation results, obtained from a standard TIMIT phonetic recognition task confined within the N-best rescoring scenario, are reported on comparative performances between the new feature-based recognizer and a recognizer using the conventional context-dependent triphone units. The results demonstrate an overall superior quality of the rescored N-best list from the feature-based recognizer over that from the triphone-based recognizer. Greater performance improvements are observed as the top number of candidate sentences increases.
Tracking nonstationary targets using a dynamical system with Markov-modulated parameters

Tracking moving targets from partial measurements is an important problem with applications in control theory and pattern recognition. This letter presents a statistical framework for modeling target motion as the output of a linear dynamical system driven by a random step function representing sequences of idealized target positions. Target trajectories are observed through a noisy nonlinear measurement function, whereas system parameters are modulated by a semi-Markov chain representing changes in target regime. An algorithm is presented for maximum-likelihood parameter estimation from a corpus of observations, and potential applications to articulatory speech recognition are discussed.<<ETX>>
A Markov model containing state-conditioned second-order non-stationarity: application to speech recognition

In this paper we report our development of a new class of hidden Markov models (HMMs) with each state characterized by a time series model which is non-stationary up to the second order. A close-form solution for the model parameter estimation is obtained based on the EM algorithm and on the matrix-calculus implementation technique. In the first set of evaluation experiments, we adopt the residual square sum, over states and over time frames within state bounds, as a quantitative measure for goodness of fit between the model and the speech data. It is observed that inclusion of state-conditioned second-order non-stationarity, implemented by use of time-varying regression coefficients, has substantially greater effects on reducing data-fitting error than increase of the regression terms while maintaining the coefficients of each term constant. In the second set, isolated-word recognition experiments, it is found that use of mix of first-order and second-order non-stationarities consistently produces higher recognition accuracy than the conventional, stationary-state HMMs.
A Review of Graphical Model Architectures for Speech Recognition

Graphs are a two-dimensional visual formalism that can be used to describe many different phenomena and are used in wide variety of fields including computer science, data and control flow, entity relationships and social networks, Petri and neural networks, software/hardware visualization, and parallel computation. The popularity of graphs is owing to their ability represent complex situations in an intuitive and visually appealing way. Statistical graphical models are a family of graphical abstractions of statistical models, where important aspects (e.g., factorization) of such models are represented using graphs. In recent years and due to a wide range of research, it has become apparent that graphical models offer a mathematically formal but widely flexible means for solving many of the problems in speech and language processing. Graphs are able to represent events at the very high level (such as relationships between linguistic classes), at the very low level (such as correlations between spectral features or acoustic landmarks), and at all levels in between (such as lexical pronunciation). A fundamental advantage of graphical models is rapidity — with a graphical model, it is possible to quickly express a novel complicated idea in an intuitive, concise, and mathematically precise way, and to speedily and visually communicate that idea between colleagues. Moreover, with the right software, it is possible to rapidly prototype that idea on a standard desktop workstation.
A generative/discriminative learning algorithm for image classification

We have developed a two-phase generative/discriminative learning procedure for the recognition of classes of objects and concepts in outdoor scenes. Our method uses both multiple types of object features and context within the image. The generative phase normalizes the description length of images, which can have an arbitrary number of extracted features of each type. In the discriminative phase, a classifier learns which images, as represented by this fixed-length description, contain the target object. We have tested the approach by comparing it to several other approaches in the literature and by experimenting with several different data sets and combinations of features. Our results, using color, texture, and structure features, show a significant improvement over previously published results in image retrieval. Using salient region features, we are competitive with recent results in object recognition
Speech feature smoothing for robust ASR

We evaluate smoothing within the context of the MVA (mean subtraction, variance normalization, and ARMA filtering) post-processing scheme for noise-robust automatic speech recognition. MVA has shown great success in the past on the Aurora 2.0 and 3.0 corpora, even though it is computationally inexpensive. MVA is applied to many acoustic feature extraction methods, and is evaluated using Aurora 2.0. We evaluate MVA post-processing on MFCCs, LPCs, PLPs, RASTA, Tandem, modulation-filtered spectrogram, and modulation cross-correlogram features. We conclude that, while effectiveness does depend on the extraction method, the majority of features benefit significantly from MVA, and the smoothing ARMA filter is an important component. It appears that the effectiveness of normalization and smoothing depends on the domain in which it is applied, being most fruitfully applied just before being scored by a probabilistic model. Moreover, since it is both effective and simple, our ARMA filter should be considered a candidate method in most noise-robust speech recognition tasks.
SVitchboard 1: Small Vocabulary Tasks from Switchboard 1

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.
Dialog act tagging using graphical models

Detecting discourse patterns, such as dialog acts (DAs), is an important factor for processing spoken conversations and meetings. Different techniques, such as hidden Markov models and neural networks, have been used to tag dialog acts in the past. A full analysis of dialog act tagging using different generative and conditional dynamic Bayesian networks (DBNs) is performed, where both conventional switching n-grams and factored language models (FLMs) are used as DBN edge implementations. Our tests on the ICSI meeting recorder dialog act (MRDA) corpus show that the factored language model implementations are better than the switching n-gram approach. Our results also show that by using virtual evidence, the label bias problem in conditional models can be avoided. Also, we find that, on a corpus such as MRDA, using the dialog acts of previous sentences to help predict current words does not improve our conditional model.