nils-herrmann/clean_scirepeval huggingface dataset:
Optimization of dynamic regimes in a statistical hidden dynamic model for conversational speech recognition

Multi-ply web forming stock jets directed through a slice from a single headbox into a forming zone between converging forming surfaces in a papermaking machine are maintained separated for a sufficient interval after leaving the slice opening to permit drainage and partial ply formation of the stock delivered by one of the stock jets to one of the forming surfaces before the stock in a second of the stock jets joins in fiber felting laminar relation with the contiguous surface of the partially formed ply. Flexible divider sheet members divide the slice chamber of a headbox into separate flow passages aligned respectively with different ones of headbox subchambers to receive streams of the stock from the respective subchambers and maintain the streams separated, and the flexible members extend a substantial distance out of the slice opening in the direction of flow of the stock jets to maintain stock jets separated for a sufficient interval after leaving the slice opening.
Speech enhancement using voice source models

Autoregressive (AR) models have been shown to be effective models of the human vocal tract during voicing. However the most common model of speech for enhancement purposes, AR process excited by white noise, fails to capture the periodic nature of voiced speech. Speech synthesis researchers have long recognized this problem and have developed a variety of sophisticated excitation models, however these models have yet to make an impact in speech enhancement. We have chosen one of the most common excitation models, the four-parameter LF model of Fant, Liljencrants and Lin (1985), and applied it to the enhancement of individual voiced phonemes. Comparing the performance of the conventional white-noise-driven AR, an impulsive-driven AR, and AR based on the LF model shows that the LF model yields a substantial improvement, on the order of 1.3 dB.
Computational Models for Speech Production

Major speech production models from speech science literature and a number of popular statistical “generative” models of speech used in speech technology are surveyed. Strengths and weaknesses of these two styles of speech models are analyzed, pointing to the need to integrate the respective strengths while eliminating the respective weaknesses. As an example, a statistical task-dynamic model of speech production is described, motivated by the original deterministic version of the model and targeted for integrated-multilingual speech recognition applications. Methods for model parameter learning (training) and for likelihood computation (recognition) are described based on statistical optimization principles integrated in neural network and dynamic system theories.
Vocal‐tract length normalization for acoustic‐to‐articulatory mapping using neural networks

A new method of estimating the overall vocal‐tract (VT) length and the normalization of acoustic parameters of different speakers is reported in this paper for acoustic‐to‐articulatory mapping. The main goal of this work was a high accuracy of VT length estimation from a short speech utterance. An articulatory model, originally developed by Maeda, was used as a reference female VT. Linear scaling was used to synthesize training data for VT lengths between 100% and 125% of the reference VT length (14.96 cm). These data had 250 utterances, resulted from different VT lengths, each containing six vowels. A neural network with two hidden layers was trained using vectors of 10 mel‐frequency cepstrum coefficients and the corresponding VT lengths of these utterances. For the same VT length range, similar test data were synthesized using the training vowels but in different contexts. With the trained network, evaluation of this method on test data has shown an average error of less than 1% and a maximum error of 3...
A statistical coarticulatory model for the hidden vocal-tract-resonance dynamics

A statistical coarticulatory model is presented for spontaneous speech recognition, where knowledge of the dynamic, target-directed behavior in the vocal tract resonance responsible for the production of highly coarticulated speech is incorporated into the recognizer design, training, and in likelihood computation. The principal advantage of the new speech model over the conventional HMM is the use of a compact, internal structure that parsimoniously represents long-span context dependence in the observable domain of speech acoustics without using additional, contextdependent model parameters. The new model is formulated mathematically as a constrained, nonstationary, and nonlinear dynamic system, for which a version of the generalized EM algorithm is developed and implemented for automatically learning the compact set of model parameters. Experiments for speech recognition using spontaneous speech data from SWITCHBOARD corpus are reported.
Computational Models for Auditory Speech Processing

Auditory processing of speech is an important stage in the closed-loop human speech communication system. A computational auditory model for temporal processing of speech is described with details of numerical solution and of the temporal information extraction method given. The model is used to process fluent speech utterances and is applied to phonetic classification using both clean and noisy speech materials. The need for integrating auditory speech processing and phonetic modeling components in machine speech recognizer design is discussed within a proposed computational framework of speech recognition motivated by the closed-loop speech chain model for integrated human speech production and perception behaviors.
Evaluation of quality of speech enhanced by HMM and AR model‐based systems

Speech enhancement algorithms have demonstrated their application potential in a wide variety of speech communication contexts in which the quality or the intelligibility of speech has been degraded by the presence of background noise: hearing aids, cellular phones, public telephones, hands‐free telephones, air‐ground communications, etc. By far the two most popular choices for model‐based speech enhancement are the Wiener‐filter‐based hidden Markov model (HMM), and the autoregressive (AR) model‐based Kalman filter. Although researchers have been studying such enhancement systems for some time, relatively little has been undertaken in comparing the quality of enhanced speech produced by these two systems. This paper studies the Wiener/HMM and AR/KF models and conducts a comprehensive comparative study of the relative quality of enhanced speech, based on utterances from the TIMIT database contaminated by simulated and sampled empirical noises. The Wiener/HMM and AR/KF comparison includes both subjective an...
SPEECH SYNTHESIS USING A PHYSIOLOGICAL ARTICULATORY MODEL WITH FEATURE-BASED RULES

A 3-D computational model of speech articulators has been developed for human-mimetic speech synthesis. The model geometry was derived from volumetric MRI data that were collected from one male speaker. A multipoint control strategy is developed to control the model, which involves three points of the articulators: the tongue tip, tongue dorsum, and the jaw. To control these points in the geometric space of the vocal tract independently, a set of weight coefficients is defined for each muscle in a specific control point. A dynamic muscle workspace is proposed to predict muscle force vectors for a control point in any arbitrary position. Muscle activation signals are generated via the dynamic workspace, and fed to the muscles to drive the model. To develop a speech synthesis system using the physiological model, this study explores some feature-based phonological rules, which provides temporally overlapping articulatory targets from a given sequence of phonetic segments. 1.
Model-based speaker normalization methods for speech recognition

A speaker normalization method using a speech generation model is proposed in order to achieve high-performance speaker adaptation with a small amount of adaptation data. The speaker- and phoneme-dependent vocal tract area function is approximated by the corresponding area function produced by the articulatory model of a standard speaker, combined with phoneme-independent feature quantities of the vocal-tract shape of the normalized target speaker as estimated from the formant frequencies of two vowels. The frequency warping functions are determined from the formant frequencies of speech calculated from the vocal-tract area functions thus obtained, and normalization of the uttered speech is performed by stretching the speech spectrum in the frequency-axis direction. Continuous phoneme recognition experiments using phoneme connection rules show that the recognition error using a gender-dependent model is reduced by about 30% in the proposed method and that recognition performance superior to that of vocal-tract length normalization is obtained. The recognition performance of the proposed method is also equivalent to that of speaker adaptation by moving vector field smoothing (VFS) using 10 phonetically balanced sentences, showing that high-performance speaker adaptation using a small amount of adaptation data can be achieved by the proposed method.
Paciic Association for Computational Linguistics Phonological Rules for Overlapping Articulatory Features in Speech Recognition

Modeling phonological units of speech is a critical issue in speech recognition. In this paper, we report our recent development of an overlapping-feature based phonological model which represents long-span contextual dependency in speech acoustics. In this model, high-level linguistic constraints are incorporated in automatic construction of the feature overlapping patterns and the associated hidden Markov model (HMM) states which represent acoustic variability. The main linguistic information explored includes morpheme and syllable boundaries, syllable constituent categories, and word stress. A consistent computational framework developed for the construction of the feature-based model and the major components of the model are described. The experimental results on use of the feature-based model as the HMM state topology for speech recognition are reported.
A layered neural network interfaced with a cochlear model for the study of speech encoding in the auditory system

Our recent development of a computational cochlear-nucleus-like network model for the study of speech encoding mechanisms associated with parts of the auditory system central to the auditory nerve is described in this paper. This network model is based on physiological grounds, and is designed to gracefully interface with a cochlear model established earlier that incorporates a biophysically motivated dynamic nonlinearity in the basilar-membrane filtering function. This study addresses a longstanding issue in auditory research; that is, how the unique spatial–temporal patterns, shaped by the cochlea's nonlinear filtering, in the auditory nerve data in response to high-level speech sounds can be transformed into a rate-place code in the auditory system in a physiologically plausible manner? Detailed neural mechanisms implemented in the model include neural inhibition, coincidence detection, short-term temporal integration of post synaptic potentials for action potential generation, and a conjectural temporal-to-rate conversion mechanism requiring the membrane time constant of a neuron to monotonically decrease with the neuron's CF. Model simulation experiments using both synthetic and natural speech utterances demonstrate that the auditory rate-place code constructed at the output of the network model is capable of reliable representation, with possible modification and/or enhancement, of the prominent spectral characteristics of the utterances displayed in wideband spectrograms. The effectiveness of the rate-place code is demonstrated, with examples taken from TIMIT corpus, to be universal across all classes of speech sounds including vowels, liquids, fricatives, nasals, and stops.
Articulatory Features and Associated Production Models Statistical Speech Recognition

A statistical approach to speech recognition is outlined which draws close parallel with closed-loop human speech communication schematized as a joint process of encoding and decoding of linguistic messages. The encoder consists of the symbolically-valued overlapping articulatory feature model and of its interface to a nonlinear task-dynamic model of speech production. A general speech recognizer architecture based on optimal decoding strategy incorporating encoder-decoder interactions is described and discussed.
Initial evaluation of hidden dynamic models on conversational speech

Conversational speech recognition is a challenging problem primarily because speakers rarely fully articulate sounds. A successful speech recognition approach must infer intended spectral targets from the speech data, or develop a method of dealing with large variances in the data. Hidden dynamic models (HDMs) attempt to automatically learn such targets in a hidden feature space using models that integrate linguistic information with constrained temporal trajectory models. HDMs are a radical departure from conventional hidden Markov models (HMMs), which simply account for variation in the observed data. We present an initial evaluation of such models on a conversational speech recognition task involving a subset of the SWITCHBOARD corpus. We show that in an N-best rescoring paradigm, HDMs are capable of delivering performance competitive with HMMs.
Speech trajectory discrimination using the minimum classification error learning

In this paper, we extend the maximum likelihood (ML) training algorithm to the minimum classification error (MCE) training algorithm for discriminatively estimating the state-dependent polynomial coefficients in the stochastic trajectory model or the trended hidden Markov model (HMM) originally proposed in Deng (1992). The main motivation of this extension is the new model space for smoothness-constrained, state-bound speech trajectories associated with the trended HMM, contrasting the conventional, stationary-state HMM, which describes only the piecewise-constant "degraded trajectories" in the observation data. The discriminative training implemented for the trended HMM has the potential to utilize this new, constrained model space, thereby providing stronger power to disambiguate the observational trajectories generated from nonstationary sources corresponding to different speech classes. Phonetic classification results are reported which demonstrate consistent performance improvements with use of the MCE-trained trended HMM both over the regular ML-trained trended HMM and over the MCE-trained stationary-state HMM.
HMM-based strategies for enhancement of speech signals embedded in nonstationary noise

An improved hidden Markov model-based (HMM-based) speech enhancement system designed using the minimum mean square error principle is implemented and compared with a conventional spectral subtraction system. The improvements to the system are: (1) incorporation of mixture components in the HMM for noise in order to handle noise nonstationarity in a more flexible manner, (2) two efficient methods in the speech enhancement system design that make the system real-time implementable, and (3) an adaptation method to the noise type in order to accommodate a wide variety of noise expected under the enhancement system's operating environment. The results of the experiments designed to evaluate the performance of the HMM-based speech enhancement systems in comparison with spectral subtraction are reported. Three types of noise-white noise, simulated helicopter noise, and multitalker (cocktail party) noise-were used to corrupt the test speech signals. Both objective (global SNR) and subjective mean opinion score (MOS) evaluations demonstrate consistent superiority of the HMM-based enhancement systems that incorporate the innovations described in this paper over the conventional spectral subtraction method.
Use of high-level linguistic constraints for constructing feature-based phonological model in speech recognition

Modeling phonological units of speech is a critical issue in speech recognition. In this paper, we report our recent development of an overlapping feature-based phonological model which gives long-span contextual dependency. We extend our earlier work by incorporating high-level linguistic constaints in automatic construction of the feature overlapping patterns. The main linguistic information explored includes morpheme, syllable, syllable constituent categories and word stress markers. We describe a consistent computational framework developed for the construction of the feature-based model, and discuss use of the model as the HMM state topology for speech recognizers.
Recovering vocal tract shapes from MFCC parameters

Recovering vocal tract shapes from the speech signal is a well known inversion problem of transformation from the articulatory system to speech acoustics. Most of the studies on this problem in the past have been focused on vowels. There have not been general methods e ective for recovering the vocal tract shapes from the speech signal for all classes of speech sounds. In this paper we describe our attempt towards speech inverse mapping by using the mel-frequency cepstrum coe cients to represent the acoustic parameters of the speech signal. An inversion method is developed based on Kalman ltering and a dynamic-system model describing the articulatory motion. This method uses an articulatory-acoustic codebook derived from Maeda's articulatory model.
Speaker clustering for speech recognition using the parameters characterizing vocal-tract dimensions

We propose speaker clustering methods based on the vocal-tract-size related articulatory parameters associated with individual speakers. Two parameters characterizing gross vocal-tract dimensions are first derived from formants of speaker-specific Japanese vowels, and are then used to cluster a total of 148 male Japanese speakers. The resultant speaker clusters are found to be significantly different from the speaker clusters obtained by conventional acoustic criteria. Japanese phoneme recognition experiments are carried out using speaker-clustered tied-state HMMs (HMNets) trained for each cluster. Compared with the baseline gender dependent model, 5.7% of recognition error reduction has been achieved based on the clustering method using vocal-tract parameters.
Estimation of Articulatory Parameters from Speech Acoustics by Kalman Filtering

This paper presents our research work of estimating articulatory states and model parameters from speech acoustics. This work represents a prerequisite for a speech recognition system based on articulatory dynamical models. The model parameter estimation was based on the vowels obtained from 590 sentences of 59 speakers from TIMIT speech database, whereas the state estimation experiments have been done using the vowels from 100 sentences of 10 speakers from TIMIT. The 10 English vowels investigated have the following transcription: /AA/, /AE/, /AH/, /AO/, /EH/, /EY/, /IH/, /IY/, /UH/ and /UW/. For each vowel, articulatory dynamical models were created using secondorder dynamical systems. The states of these models represent the positions of articulators such as lips, tongue and pharynx. We used 8 state parameters to represent these articulators and 3 formant frequencies to represent the acoustic observation vectors. The nonlinear relationship between articulatory state vector and speech acoustics has been approximated using a piecewise linear approximation on small regions in the articulatory space. This linearization was performed using a codebook of 610,000 pairs of articulatory and acoustic vectors created using the Metropolis algorithm. The whole codebook was then linearized on about 7,500 regions using a vector quantization method. The articulatory model parameters and states were estimated using the EM (expectation-maximization) algorithm and Kalman ltering techniques. The results obtained in estimating the articulatory parameters encourage us to apply this technique to automatic speech recognition.
Integrated multilingual speech recognition-impact on chinese spoken language processing

The notion of integrated multilingualism is introduced as the basis for a novel approach to multilingual speech recog nition This approach enables training of the recognizer using the data from only source language s The trained recognizer is nevertheless deployable directly to new tar get languages The performance of the recognizer is incre mentally improved via a language adaptation strategy In this paper an overview is provided of a language universal feature based phonological model and of a dynamic pho netic model that in combination constitute the general framework of integrated multilingual speech recognition Some related speech recognition experiments in connec tion with this framework are described Finally impact of the new approach to Chinese spoken language processing is analyzed in terms of the need to develop a successful and uniform speech recognition strategy for a wide variety of Chinese dialects with little or no training data required for the dialects INTRODUCTION
Speech analysis and recognition using interval statistics generated from a composite auditory model

A modeling approach to auditory speech analysis and recognition is proposed and evaluated, where a composite auditory model is used to generate parallel sets of auditory-nerve instantaneous firing rates (IFRs) along the spatial dimension, followed by a processing stage that constructs from the IFRs the interval statistics in a form called the interpeak interval histogram (IPIH). A speech preprocessor is designed that performs transformation on the auditory IPIHs and interfaces the IPIH-based auditory representation with a hidden Markov model-based (HMM-based) speech recognizer. The results demonstrate that the new preprocessor consistently outperforms the conventional mel frequency cepstral coefficient-based (MFCC-based) preprocessor for the signal-to-noise ratio (SNR) level up to at least 16 dB.
Speaker-independent phonetic classification using hidden Markov models with mixtures of trend functions

We extend the nonstationary-state or trended hidden Markov model (HMM) from the previous single-trend formulation (Deng, 1992; Deng et al., 1994) to the current mixture-trended one. This extension is motivated by the observation of wide variations in the trajectories of the acoustic data in fluent, speaker-independent speech associated with a fixed underlying linguistic unit. It is also motivated by potential use of mixtures of trend functions to characterize heterogeneous time-varying data generated from distinctive sources such as the speech signals collected from different microphones or from different telephone channels. We show how HMMs with mixtures of trend functions can be implemented simply in the already well-established single-trend HMM framework via the device of expanding each state into a set of parallel states. Details of a maximum-likelihood-based (ML-based) algorithm are given for estimating state-dependent mixture trajectory parameters in the model. Experimental results on the task of classifying speaker-independent vowels excised from the TIMIT data base demonstrate consistent performance improvement using phonemic mixture-trended HMMs over their single-trend counterpart.