nils-herrmann/clean_scirepeval huggingface dataset:
A latent-variable modelling approach to the acoustic-to-articulatory mapping problem. I

We present a latent variable approach to the acoustic-toarticulatory mapping problem, where different vocal tract configurations can give rise to the same acoustics. In latent variab le modelling, the combined acoustic and articulatory data are ass umed to have been generated by an underlying low-dimensional proce ss. A parametric probabilistic model is estimated and mappings a re derived from the respective conditional distributions. This as the advantage over other methods, such as articulatory codeboo ks or neural networks, of directly addressing the nonuniqueness problem. We demonstrate our approach with electropalatographi c and acoustic data from the ACCOR database.
RETRIEVAL SYSTEM

This paper described the THISL spoken document retrieval system for British and North American Broadcast News. The system is based on the ABBOT large vocabulary speech recognizer, using a recurrent network acoustic model, and a probabilistic text retrieval system. We discuss the development of a realtime British English Broadcast News system, and its integration into a spoken document retrieval system. Detailed evaluation is performed using a similar North American Broadcast News system, to take advantage of the TREC SDR evaluation methodology. We report results on this evaluation, with particular reference to the effect of query expansion and of automatic segmentation algorithms.
The THISL spoken document retrieval project

In particular it is concerned with the production of a demonstrator news-on-demand system to navigate an archive of BBC news broadcasts. Prototype systems based on both British and North American broadcast news have been constructed. The North American system has been successfully evaluated within the framework of the TREC-6 and TREC-7 spoken document retrieval tracks, and the system based on BBC TV and radio news archives will be evaluated by BBC R&D.
Experimental evaluation of latent variable models for dimensionality reduction

We use electropalatographic (EPG) data as a test bed for dimensionality reduction methods based in latent variable modelling, in which an underlying lower dimension representation is inferred directly from the data. Several models (and mixtures of them) are investigated, including factor analysis and the generative topographic mapping. Experiments indicate that nonlinear latent variable modelling reveals a low-dimensional structure in the data inaccessible to the investigated linear models.
Confidence Measures for Evaluating Pronunciation Models

In this paper, we investigate the use of confidence measures for the evaluation of pronunciation models and the employment of these evaluations in an automatic baseform learning process. The confidence measures and pronunciation models are obtained from the ABBOT hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN) Large Vocabulary Continuous Speech Recognition (LVCSR) system . Experiments were carried out for a number of baseform learning schemes using the ARPA North American Business News (NAB) and the Broadcast News (BN) corpora from which it was found that a confidence measure based scheme provided the largest reduction in Word Error Rate (WER).
Acoustic confidence measures for segmenting broadcast news

In this paper we define an acoustic confidence measure based on the estimates of local posterior probabilities produced by a HMM/ANN large vocabulary continuous speech recognition system. We use this measure to segment continuous audio into regions where it is and is not appropriate to expend recognition effort. The segmentation is computationally inexpensive and provides reductions in both overall word error rate and decoding time. The technique is evaluated using material from the Broadcast News corpus.
Confidence measures derived from an acceptor HMM

In this paper we define a number of confidence measures derived from an acceptor HMM and evaluate their performance for the task of utterance verification using the North American Business News (NAB) and Broadcast News (BN) corpora. Results are presented for decodings made at both the word and phone level which show the relative profitability of rejection provided by the diverse set of confidence measures. The results indicate that language model dependent confidence measures have reduced performance on BN data relative to that for the more grammatically constrained NAB data. An explanation linking the observations that rejection is more profitable for noisy acoustics, for a reduced vocabulary and at the phone level is also given.
Retrieval of broadcast news documents with the THISL system

This paper describes a spoken document retrieval system, combining the ABBOT large vocabulary continuous speech recognition (LVCSR) system developed by Cambridge University, Sheffield University and SoftSound, and the PRISE information retrieval engine developed by NIST. The system was constructed to enable us to participate in the TREC 6 Spoken Document Retrieval experimental evaluation. Our key aims in this work were to produce a complete system for the SDR task, to investigate the effect of a word error rate of 30-50% on retrieval performance and to investigate the integration of LVCSR and word spotting in a retrieval task.
Document space models using latent semantic analysis

In this paper, an approach for constructing mixture language models (LMs) based on some notion of semantics is discussed. To this end, a technique known as latent semantic analysis (LSA) is used. The approach encapsulates corpus-derived semantic information and is able to model the varying style of the text. Using such information , the corpus texts are clustered in an unsuper-vised manner and mixture LMs are automatically created. This work builds on previous work in the eld of information retrieval which was recently applied by Bel-legarda et. al. to the problem of clustering words by semantic categories. The principal contribution of this work is to characterize the document space resulting from the LSA modeling and to demonstrate the approach for mixture LM application. Comparison is made between manual and automatic clustering in order to elucidate how the semantic information is expressed in the space. It is shown that, using semantic information, mixture LMs performs better than a conventional single LM with slight increase of computational cost.
THE USE OF RECURRENT NEURAL NETWORKS IN CONTINUOUS SPEECH RECOGNITION

This chapter describes a use of recurrent neural networks (i.e., feedback is incorporated in the computation) as an acoustic model for continuous speech recognition. The form of the recurrent neural network is described along with an appropriate parameter estimation procedure. For each frame of acoustic data, the recurrent network generates an estimate of the posterior probability of of the possible phones given the observed acoustic signal. The posteriors are then converted into scaled likelihoods and used as the observation probabilities within a conventional decoding paradigm (e.g., Viterbi decoding). The advantages of using recurrent networks are that they require a small number of parameters and provide a fast decoding capability (relative to conventional, large-vocabulary, HMM systems)3.
Phone deactivation pruning in large vocabulary continuous speech recognition

Introduces a new pruning strategy for large vocabulary continuous speech recognition based on direct estimates of local posterior phone probabilities. This approach is well suited to hybrid connectionist/hidden Markov model systems. Experiments on the Wall Street Journal task using a 20000 word vocabulary and a trigram language model have demonstrated that phone deactivation pruning can increase the speed of recognition-time search by up to a factor of 10, with a relative increase in error rate of less than 2%.
Efficient evaluation of the LVCSR search space using the NOWAY decoder

This article further develops and analyses the large vocabulary continuous speech recognition (LVCSR) search strategy reported by Renals and Hochberg (see Proc. ICASSP '95, p.596-9, 1995). In particular, the posterior-based phone deactivation pruning approach has been extended to include phone-dependent thresholds and an improved estimate of the least upper bound on the utterance log-probability has been developed. Analysis of the pruning procedures and of the search's interaction with the language model has also been performed. Experiments were carried out using the ARPA North American Business News task with a 20,000 word vocabulary and a trigram language model. As a result of these improvements and analyses, the computational cost of the recognition process performed by the NOWAY decoder has been substantially reduced.
The 1995 ABBOT LVCSR system for multiple unknown microphones

ABBOT is a hybrid (connectionist-hidden Markov model) large-vocabulary speech recognition (LVCSR) system, developed at Cambridge University. In this system, a recurrent network maps each acoustic vector to an estimate of the posterior probabilities of the phone classes, which are used as observation probabilities within an HMM. This paper describes the system which participated in the November 1995 ARPA Hub-3 multiple unknown microphones (MUM) evaluation of continuous speech recognition systems, under the guise of the CU-CON system. The emphasis of the paper is on the changes made to the 1994 ABBOT system, specifically to accommodate the H3 task. This includes improved acoustic modelling using limited word-internal context-dependent models, training on the Wall Street Journal secondary channel database, and using the linear input network for speaker and environmental adaptation. Experimental results are reported for various test and development sets from the November 1994 and 1995 ARPA benchmark tests.
Recent improvements to the ABBOT large vocabulary CSR system

ABBOT is the hybrid connectionist-hidden Markov model (HMM) large-vocabulary continuous speech recognition (CSR) system developed at Cambridge University. This system uses a recurrent network to estimate the acoustic observation probabilities within an HMM framework. A major advantage of this approach is that good performance is achieved using context-independent acoustic models and requiring many fewer parameters than comparable HMM systems. This paper presents substantial performance improvements gained from new approaches to connectionist model combination and phone-duration modeling. Additional capability has also been achieved by extending the decoder to handle larger vocabulary tasks (20000 words and greater) with a trigram language model. This paper describes the modifications to the system and experimental results are reported for various test and development sets from the November 1992, 1993, and 1994 ARPA evaluations of spoken language systems.
Speaker-adaptation for hybrid HMM-ANN continuous speech recognition system

It is well known that recognition performance degrades signi cantly when moving from a speakerdependent to a speaker-independent system. Traditional hidden Markov model (HMM) systems have successfully applied speaker-adaptation approaches to reduce this degradation. In this paper we present and evaluate some techniques for speaker-adaptation of a hybrid HMM-arti cial neural network (ANN) continuous speech recognition system. These techniques are applied to a well trained, speaker-independent, hybrid HMM-ANN system and the recognizer parameters are adapted to a new speaker through o -line procedures. The techniques are evaluated on the DARPA RM corpus using varying amounts of adaptation material and different ANN architectures. The results show that speaker-adaptation within the hybrid framework can substantially improve system performance.
DECODER TECHNOLOGY FOR CONNECTIONIST LARGE VOCABULARY SPEECH RECOGNITION

The search problem in large vocabulary continuous speech recognition (LVCSR) is to locate the most probable string of words for a spoken utterance given the acoustic signal and a set of sentence models. Searching the space of possible utterances is difficult because of the large vocabulary size and the complexity imposed when long-span language models are used. This report describes an efficient search procedure and its software embodiment in a decoder, NOWAY, which has been incorporated in ABBOT, a hybrid connectionist/ hidden Markov model (HMM) LVCSR system [15]. The search algorithm is based on stack decoding and uses both likelihoodand posterior-based pruning. The use of the posterior-based phone deactivation pruning techniques is well-suited to hybrid connectionist/HMM systems because posterior phone probabilities are directly computed by the connectionist acoustic model. The single-pass decoder has been evaluate on the large vocabulary North American Business News task using a 20,000 word vocabulary and a trigram language model. These results indicate that phone deactivation pruning increased the search speed by an order of magnitude while incurring 2% or less relative search error. Using a pentium-based PC system, evaluation quality decoding (less than 3% relative search error) was available with execution speeds 2–5 times slower than realtime, and realtime decoding was available at the cost of 4–12% relative search error.
Efficient search using posterior phone probability estimates

We present a novel, efficient search strategy for large vocabulary continuous speech recognition (LVCSR). The search algorithm, based on stack decoding, uses posterior phone probability estimates to substantially increase its efficiency with minimal effect on accuracy. In particular, the search space is dramatically reduced by phone deactivation pruning where phones with a small local posterior probability are deactivated. This approach is particularly well-suited to hybrid connectionist/hidden Markov model systems because posterior phone probabilities are directly computed by the acoustic model. On large vocabulary tasks, using a trigram language model, this increased the search speed by an order of magnitude, with 2% or less relative search error. Results from a hybrid system are presented using the Wall Street Journal LVCSR database for a 20,000 word task using a backed-off trigram language model. For this task, our single-pass decoder took around 15x realtime on an HP735 workstation. At a cost of 7% relative search error, the decoding time can be speeded up to approximately realtime.
The 1994 Abbot hybrid connectionist-HMM large vocabulary recognition system.

ABBOT is the hybrid connectionist-hidden Markov model largevocabulary speech recognition system developed at Cambridge University. In this system, a recurrent network maps each acoustic vector to an estimate of the posterior probabilities of the phone classes. The maximum likelihood word string is then extracted using Markov models. As in traditional hidden Markov models, the Markov process is used to model the lexical and language model constraints. This paper describes the system which participated in the November 1994 ARPA evaluation of continuous speech recognition systems. The emphasis of the paper is on the differences between the 1993 and 1994 versions of the ABBOT system. This includes the utilization of a larger training corpus (SI284 versus SI84), the extension of the lexicon from 5,000 words to 65,000 words, the application of a trigram language model, and the development of a near-realtime single-pass decoder well suited for the hybrid approach. Experimental results are reported for various test and development sets from the November 1992, 1993 and 1994 ARPA benchmark tests.
WSJCAMO: a British English speech corpus for large vocabulary continuous speech recognition

A significant new speech corpus of British English has been recorded at Cambridge University. Derived from the Wall Street Journal text corpus, WSJCAMO constitutes one of the largest corpora of spoken British English currently in existence. It has been specifically designed for the construction and evaluation of speaker-independent speech recognition systems. The database consists of 140 speakers each speaking about 110 utterances. This paper describes the motivation for the corpus, the processes undertaken in its construction and the utilities needed as support tools. All utterance transcriptions have been verified and a phonetic dictionary has been developed to cover the training data and evaluation tasks. Two evaluation tasks have been defined using standard 5000 word bigram and 20000 word trigram language models. The paper concludes with comparative results on these tasks for British and American English.
Human-human haptic collaboration in cyclical Fitts' tasks

Understanding how humans assist each other in haptic interaction teams could lead to improved robotic aids to solo human dextrous manipulation. Inspired by experiments reported in Reed et al. (2004), which suggested two-person haptically interacting teams could achieve a lower movement time (MT) than individuals for discrete aiming movements of specified accuracy, we report that two-person teams (dyads) can also achieve lower MT for cyclical, continuous aiming movements. We propose a model, called endpoint compromise, for how the intended endpoints of both subjects' motion combine during haptic interaction; it predicts a ratio of /spl radic/2 between slopes of MT fits for individuals and dyads. This slope ratio prediction is supported by our data.
Dynamic systems identification with Gaussian processes

This paper describes the identification of nonlinear dynamic systems with a Gaussian process (GP) prior model. This model is an example of the use of a probabilistic non-parametric modelling approach. GPs are flexible models capable of modelling complex nonlinear systems. Also, an attractive feature of this model is that the variance associated with the model response is readily obtained, and it can be used to highlight areas of the input space where prediction quality is poor, owing to the lack of data or complexity (high variance). We illustrate the GP modelling technique on a simulated example of a nonlinear system.
Hierarchical Gaussian process mixtures for regression

As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields. However, two major problems arise when the model is applied to a large data-set with repeated measurements. One stems from the systematic heterogeneity among the different replications, and the other is the requirement to invert a covariance matrix which is involved in the implementation of the model. The dimension of this matrix equals the sample size of the training data-set. In this paper, a Gaussian process mixture model for regression is proposed for dealing with the above two problems, and a hybrid Markov chain Monte Carlo (MCMC) algorithm is used for its implementation. Application to a real data-set is reported.
Rehabilitation Robot Cell for Multimodal Standing-Up Motion Augmentation

The paper presents a robot cell for multimodal standing-up motion augmentation. The robot cell is aimed at augmenting the standing-up capabilities of impaired or paraplegic subjects. The setup incorporates the rehabilitation robot device, functional electrical stimulation system, measurement instrumentation and cognitive feedback system. For controlling the standing-up process a novel approach was developed integrating the voluntary activity of a person in the control scheme of the rehabilitation robot. The simulation results demonstrate the possibility of “patient-driven” robot-assisted standing-up training. Moreover, to extend the system capabilities, the audio cognitive feedback is aimed to guide the subject throughout rising. For the feedback generation a granular synthesis method is utilized displaying high-dimensional, dynamic data. The principle of operation and example sonification in standing-up are presented. In this manner, by integrating the cognitive feedback and “patient-driven” actuation systems, an effective motion augmentation system is proposed in which the motion coordination is under the voluntary control of the user.
Switching and Learning in Feedback Systems: European Summer School on Multi-Agent Control, Maynooth, Ireland, 8-10 September 2003: Revised Lectures and Selected Papers

Switching and Control.- From Feedback Control to Complexity Management: A Personal Perspective.- Convex Cones, Lyapunov Functions, and the Stability of Switched Linear Systems.- Survey of Explicit Approaches to Constrained Optimal  Analysis of Some Methods for Reduced Rank Gaussian Process Regression.- Filtered Gaussian Processes for Learning with Large Data-Sets.- Self-tuning Control of Non-linear Systems Using Gaussian Process Prior  Prediction at a Noisy Input and Application to Iterative Multiple-Step Ahead Forecasting of Time-Series.- Nonlinear Predictive Control with a Gaussian Process Model.- Applications of Switching & Learning.- Control of Yaw Rate and Sideslip in 4-Wheel Steering Cars with Actuator Constraints.- A Second-Order Cone Bounding Algorithm for Robust Minimum Variance Beamforming.- Joint Optimization of Wireless Communication and Networked Control Systems.- Reconciliation of Inconsistencies in the Theory of Linear Systems.- An Introduction to Nonparametric Hierarchical Bayesian Modelling with a Focus on Multi-agent Learning.- Simultaneous Localization and Surveying with Multiple
Gait phase effects in mobile interaction

One problem evaluating mobile and wearable devices is that they are used in mobile settings, making it hard to collect usability data. We present a study of tap-based selection of on-screen targets whilst walking and sitting, using a PocketPC instrumented with an accelerometer to collect information about user activity at the time of each tap. From these data the user's gait can be derived, and this is then used to investigate preferred tapping behaviour relative to gait phase, and associated tap accuracy. Results showed that users were more accurate sitting than walking. When walking there were phase regions with significantly increased tap likelihood, and these regions had significantly lower error rates, and lower error variability. This work represents an example of accelerometer-instrumented mobile usability analysis, and the results give a quantitative understanding of the detailed interactions taking place when on the move, allowing us to develop better mobile interfaces.
AN ADAPTIVE NONPARAMETRIC CONTROLLER FOR A CLASS OF NONMINIMUM PHASE NON-LINEAR SYSTEM

This paper addresses the problem of controlling a class of unknown nonlinear systems with unstable inverse, by using a generalized minimum variance approach. We highlight the limitations of the classic cost function used in this context and we propose a new one based on the Generalized Feedback Linearization concept. The adaptive control algorithm uses a nonparametric model, which provides an estimate of the mean and variance of the system output. Simulation examples illustrate the main characteristics and limitation of the proposed approach.
GpsTunes: controlling navigation via audio feedback

We combine the functionality of a mobile Global Positioning System (GPS) with that of an MP3 player, implemented on a PocketPC, to produce a handheld system capable of guiding a user to their desired target location via continuously adapted music feedback. We illustrate how the approach to presentation of the audio display can benefit from insights from control theory, such as predictive 'browsing' elements to the display, and the appropriate representation of uncertainty or ambiguity in the display. The probabilistic interpretation of the navigation task can be generalised to other context-dependent mobile applications. This is the first example of a completely handheld location- aware music player. We discuss scenarios for use of such systems.