nils-herrmann/clean_scirepeval huggingface dataset:
Introduction to Wavelets and Wavelet Transforms: A Primer

1.  Filter Banks and the Discrete Wavelet Transform. 4. Bases, Orthogonal Bases, Biorthogonal Bases, Frames, Tight Frames, and Unconditional Bases. 5. The Scaling Function and Scaling Coefficients, Wavelet and Wavelet Coefficients. 6. Regularity, Moments, and Wavelet System Design. 7. Generalizations of the Basic Multiresolution Wavelet System. 8. Filter Banks and Transmultiplexers. 9. Calculation of the Discrete Wavelet Transform. 10. Wavelet-Based Signal Processing and Applications. 11.  Derivations for Section on Properties. Appendix C. Matlab Programs. Index.
Modulated filter banks and wavelets-a general unified theory

This paper generalizes and unifies well-known results on modulated filter banks (MFBs) and modulated wavelet tight frames (MWTFs). It classifies MFBs based on the discrete cosine or sine transforms that they are associated with. By proper choice of the form of modulation the perfect reconstruction (PR) conditions are seen to be (surprisingly) identical for all classes of MFBs. This has the interesting consequence that optimal MFB prototype designs can be shared across MFB classes. For some classes of MFBs associated MWTFs do not exist, while for others they do. The results cover both orthogonal and biorthogonal MFBs; and the filters could be arbitrary sequences in l/sup 2/(Z).
Thoughts on least squared-error optimal windows

Recently, a simple and versatile method for the design of linear phaser FIR filters with spline transition bands and optimal in a least-squared sense was introduced. The following question is raised: Given an arbitrary window, say, for example, a Hamming window, does there exist a transition function (like the spline function above) such that the Hamming window is least-squares optimal? A related question is the following: Given a transition function, does there exist a window sequence w(n) such that the least squared optimal FIR filter is given by g(n)w(n)? This correspondence shows that all windows have associated transition functions that make them least-squared optimal. For every window, there exists a transition function that makes it superoptimal.
Speech word recognition method and speech word identification system

To provide a speech recognition method and a device using a phoneme system with tone.  An acoustic signal processing method and a device for speech recognition are constituted as follows: (1) Each syllable is decomposed into two phonemes of equal length and complexity. The first phoneme is a prime, and the second phoneme is a toneme. (2) High, crescendo, low, decrescendo and silent tone are assigned to each toneme. (3) No tone value is assigned to the preme. (4) Pitches are continuously detected and processed in the same method as energy and cepstrum by a hidden Markov model to predict the tone of the toneme. (5) The tone of the syllable is provided as the tone of its constituent toneme.
A continuous speaker-independent putonghua dictation system

We describe new methods for continuous putonghua speech recognition. We have augmented the IBM HMM-based continuous speech recognition system with the following features. First, we treat tones in putonghua as attributes of certain phonemes, instead of syllables. We call those phonemes with tone tonemes. Second, instantaneous pitch is treated as a variable in the acoustic feature vector, in the same way as cepstra or energy. Third, by designing a set of word-segmentation rules to convert the continuous Chinese text into segmented text, the trigram language model works effectively. By applying those new methods, a speaker-independent, very-large-vocabulary continuous putonghua dictation system can be constructed.
Some Results in the Theory Modulated Filter Banks and Modulated Wavelet Tight Frames

Perfect reconstruction (PR) FIR filter banks, obtained by modulation of a linear-phase, lowpass, prototype filter and of length 2 Mm are well known. Recently, PR modulated filter banks (MFBs) with the analysis and synthesis banks obtained from different prototypes have been reported. This paper describes a general form of modulation that includes modulations used in the literature. This modulation depends on an integer parameter, the modulation phase . The PR property is characterized for MFBs with finite and infinite impulse response filters. The MFB PR problem reduces to roughly M /2 two-channel PR problems. A natural dichotomy in the PR conditions leads us to the concepts of Type 1 and Type 2 MFBs. Unitary MFBs are characterized by the M /2 two-channel PR filter banks also being unitary (for FIR filters of length N = 2 Mm , these results are given in (Malvar, ; Koilpillai and Vaidyanathan,  Apr. 1992, 770–783)). We also give a necessary and sufficient condition for a large class (including FIR) unitary MFB prototypes to have symmetric (even or odd) prototype filters, and exhibit unitary MFBs without symmetric prototypes. A parameterization of all FIR unitary MFBs is also given. An efficient design procedure for FIR unitary MFBs is developed. It turns out that MFBs can be implemented efficiently using Type III and Type IV DCTs. Compactly supported modulated wavelet tight frames are shown to exist and completely parameterized. K -regular modulated WTFs are designed numerically and analytically by solving a set of non-linear equations over the parameters. Design of optimal modulated WTFs for the representation of any given signal is described with examples, and this is used to design smooth modulated WTFs.
On cosine-modulated wavelet orthonormal bases

Multiplicity M, K-regular, orthonormal wavelet bases (that have implications in transform coding applications) have previously been constructed by several authors. The paper describes and parameterizes the cosine-modulated class of multiplicity M wavelet tight frames (WTFs). In these WTFs, the scaling function uniquely determines the wavelets. This is in contrast to the general multiplicity M case, where one has to, for any given application, design the scaling function and the wavelets. Several design techniques for the design of K regular cosine-modulated WTFs are described and their relative merits discussed. Wavelets in K-regular WTFs may or may not be smooth, Since coding applications use WTFs with short length scaling and wavelet vectors (since long filters produce ringing artifacts, which is undesirable in, say, image coding), many smooth designs of K regular WTFs of short lengths are presented. In some cases, analytical formulas for the scaling and wavelet vectors are also given. In many applications, smoothness of the wavelets is more important than K regularity. The authors define smoothness of filter banks and WTFs using the concept of total variation and give several useful designs based on this smoothness criterion. Optimal design of cosine-modulated WTFs for signal representation is also described. All WTFs constructed in the paper are orthonormal bases.
Nonlinear recovery of sparse signals from narrowband data

This paper describes the connection between a certain signal recovery problem and the decoding of Reed-Solomon codes. It is shown that any algorithm for decoding Reed-Solomon codes (over finite fields) can be used to recover wide-band signals (over the real/complex field) from narrow-band information. It also shows that a signal with at most N/sub t/ frequency samples can be recovered from any contiguous band of 2N/sub t/ frequency samples.
Factorization approach to unitary time-varying filter bank trees and wavelets

A complete factorization of all optimal (in terms of quick transition) time-varying FIR unitary filter bank tree topologies is obtained. This has applications in adaptive subband coding, tiling of the time-frequency plane and the construction of orthonormal wavelet and wavelet packet bases for the half-line and interval. For an M-channel filter bank the factorization allows one to construct entry/exit filters that allow the filter bank to be used on finite signals without distortion at the boundaries. One of the advantages of the approach is that an efficient implementation algorithm comes with the factorization. The factorization can be used to generate filter bank tree-structures where the tree topology changes over time. Explicit formulas for the transition filters are obtained for arbitrary tree transitions. The results hold for tree structures where filter banks with any number of channels or filters of any length are used. Time-varying wavelet and wavelet packet bases are also constructed using these filter bank structures. the present construction of wavelets is unique in several ways: 1) the number of entry/exit functions is equal to the number of entry/exit filters of the corresponding filter bank; 2) these functions are defined as linear combinations of the scaling functions-other methods involve infinite product constructions; 3) the functions are trivially as regular as the wavelet bases from which they are constructed.
Analysis and comparison of two speech feature extraction/compensation algorithms

Two feature extraction and compensation algorithms, feature-space minimum phone error (fMPE), which contributed to the recent significant progress in conversational speech recognition, and stereo-based piecewise linear compensation for environments (SPLICE), which has been used successfully in noise-robust speech recognition, are analyzed and compared. These two algorithms have been developed by very different motivations and been applied to very different speech-recognition tasks as well. While the mathematical construction of the two algorithms is ostensibly different, in this report, we establish a direct link between them. We show that both algorithms in the run-time operation accomplish feature extraction/compensation by adding a posterior-based weighted sum of "correction vectors," or equivalently the column vectors in the fMPE projection matrix, to the original, uncompensated features. Although the published fMPE algorithm empirically motivates such a feature extraction, operation as "a reasonable starting point for training" our analysis proves that it is a natural consequence of the rigorous minimum mean square error (MMSE) optimization rule as developed in SPLICE. Further, we review and compare related speech-recognition results with the use of fMPE and SPLICE algorithms. The results demonstrate the effectiveness of discriminative training on the feature extraction parameters (i.e., projection matrix in fMPE and equivalently correction vectors in SPLICE). The analysis and comparison of the two algorithms provide useful insight into the strong success of fMPE and point to further algorithm improvement and extension.
Learning statistically characterized resonance targets in a hidden trajectory model of speech coarticulation and reduction

We report our new development of a hidden trajectory model for co-articulated, time-varying patterns of speech. The model uses bi-directional filtering of vocal tract resonance targets to jointly represent contextual variation and phonetic reduction in speech acoustics. A novel maximum-likelihood-based learning algorithm is presented that accurately estimates the distributional parameters of the resonance targets. The results of the estimates are analyzed and shown to be consistent with all the relevant acoustic-phonetic facts and intuitions. Phonetic recognition experiments demonstrate that the model with more rigorous target training outperforms the most recent earlier version of the model, producing 17.5% fewer errors in N-best rescoring.
Spoken language understanding

This article is intended to serve as an introduction to the field of statistical SLU, based on the mainstream statistical modeling approach that shares a similar mathematical framework with many other statistical pattern recognition applications such as speech recognition. In particular, we formulated a number of statistical models for SLU in the literature as extensions to HMMs as segment models, where a multiple-word block (segment) with word dependency is generated from each underlying Markov state corresponding to each individual semantic slot defined from the application domain. In the past, due partly to its nature of symbolic rather than numeric processing, the important field of SLU in human language technology has not been widely exposed to the signal processing research community. However, many key techniques in SLU originated from statistical signal processing. And because SLU is becoming increasingly important, as one major target application area of ASR that has been dear to many signal processing researchers, we contribute this article to provide a natural bridge between ASR and SLU in methodological and mathematical foundation. It is our hope that when the mathematical basis of SLU becomes well known through this introductory article, more powerful techniques established by signal processing researchers may further advance SLU to form a solid application area, making speech technology a successful component for intelligent human-machine communication.
A hidden trajectory model with bi-directional target filtering: cascaded vs. integrated implementation for phonetic recognition

We present a novel acoustic model of speech, based on statistical hidden trajectory modeling (HTM) with bi-directional vocal tract resonance (VTR) target filtering, for speech recognition. The HTM consists of two stages of the generative process of speech: from the phone sequence to VTR dynamics and then to the cepstrum-based acoustic observation. Two types of model implementation are detailed, one with straightforward two-stage cascading, and another which integrates over the statistical distribution of VTR in model construction and in computing acoustic likelihood. With the use of first-order Taylor series approximation to the nonlinearity in the VTR-to-cepstrum prediction component of HTM, the acoustic likelihood is established in an analytical form. It is a Gaussian with the time-varying mean that gives structured long-span context dependence over the entire utterance, and with the dynamically adjusted variance proportional to the squared "local slope" in the nonlinear mapping function from VTR to cepstrum. When the HTM parameters are trained via maximizing this "integrated" likelihood, dramatic reduction of an upper error bound is achieved in the standard TIMIT phonetic recognition task using a large-scale N-best rescoring paradigm.
A Generative Modeling Framework for Structured Hidden Speech Dynamics

We outline a structured speech model, as a special and perhaps extreme form of probabilistic generative modeling. The model is equipped with long-contextual-span capabilities that are missing in the HMM approach. Compact (and physically meaningful) parameterization of the model is made possible by the continuity constraint in the hidden vocal tract resonance (VTR) domain. The target-directed VTR dynamics jointly characterize coarticulation and incomplete articulation (reduction). Preliminary evaluation results are presented on the standard TIMIT phonetic recognition task, showing the best result in this task reported in the literature without using many heterogeneous classifier combinations. The pros and cons of our structured generative modeling approach, in comparison with the structured discriminative classification approach, are discussed.
Evaluation of a long-contextual-Span hidden trajectory model and phonetic recognizer using a* lattice search

A long-contextual-span Hidden Trajectory Model (HTM) developed recently captures underlying dynamic structure of speech coarticulation and reduction using a highly compact set of context-independent parameters. However, the longspan nature of the HTM makes it difficult to develop efficient search algorithms for its full evaluation. In this paper, we describe our initial effort in meeting this challenge. The basic search algorithm is time-asynchronous A*. Given the structural complexity of the long-span HTM, special considerations are needed to take into account the fact that the HTM score for each frame depends on the model parameters associated with a variable number of adjacent phones. Specifically, we present details on how the nodes and links in the lattices are expanded via look-ahead, how the A* heuristics are estimated, and what pruning strategies are applied to speed up the search. The experiments on TIMIT phonetic recognition show the capability of our newly developed lattice search algorithm in evaluating billions of hypotheses based on long-span HTM scores. The results significantly extend our earlier work from N-best rescoring to A* search over lattices.
Multi-sensory speech processing: incorporating automatically extracted hidden dynamic information

We describe a novel technique for multi-sensory speech processing for enhancing noisy speech and for improved noise-robust speech recognition. Both air- and bone-conductive microphones are used to capture speech data where the bone sensor contains virtually noise-free hidden dynamic information of clean speech in the form of formant trajectories. The distortion in the bone-sensor signal such as teeth-clacking and noise leakage can be effectively removed by making use of the automatically extracted formant information from the bone-sensor signal. This paper reports an improved technique for synthesizing speech waveforms based on the LPC cepstra computed analytically from the formant trajectories. When this new signal stream is fused with the other available speech data streams, we achieved improved performance for noisy speech recognition.
Dynamic compensation of HMM variances using the feature enhancement uncertainty computed from a parametric model of speech distortion

This paper presents a new technique for dynamic, frame-by-frame compensation of the Gaussian variances in the hidden Markov model (HMM), exploiting the feature variance or uncertainty estimated during the speech feature enhancement process, to improve noise-robust speech recognition. The new technique provides an alternative to the Bayesian predictive classification decision rule by carrying out an integration over the feature space instead of over the model-parameter space, offering a much simpler system implementation, lower computational cost, and dynamic compensation capabilities at the frame level. The computation of the feature enhancement variances is carried out using a probabilistic and parametric model of speech distortion, free from the use of any stereo training data. Dynamic compensation of the Gaussian variances in the HMM recognizer is derived, which is simply enlarging the HMM Gaussian variances by the feature enhancement variances. Experimental evaluation using the full Aurora2 test data sets demonstrates a significant digit error rate reduction, averaged over all noisy and signal-to-noise-ratio conditions, compared with the baseline that did not exploit the enhancement variance information. When the true enhancement variances are used, further dramatic error rate reduction is observed, indicating the strong potential for the new technique and the strong need for high accuracy in estimating the variances associated with feature enhancement. All the results, using either the true variances of the enhanced features or the estimated ones, show that the greatest contribution to recognizer's performance improvement is due to the use of the uncertainty for the static features, next due to the delta features, and the least due to the delta-delta features.
A Speech-Centric Perspective for Human-Computer Interface: A Case Study

Speech technology has been playing a central role in enhancing human-machine interactions, especially for small devices for which graphical user interface has obvious limitations. The speech-centric perspective for human-computer interface advanced in this paper derives from the view that speech is the only natural and expressive modality to enable people to access information from and to interact with any device. In this paper, we describe some recent work conducted at Microsoft Research, aimed at the development of enabling technologies for speech-centric multimodal human-computer interaction. In particular, we present a case study of a prototype system, called MapPointS, which is a speech-centric multimodal map-query application for North America. This prototype navigation system provides rich functionalities that allow users to obtain map-related information through speech, text, and pointing devices. Users can verbally query for state maps, city maps, directions, places, nearby businesses and other useful information within North America. They can also verbally control applications such as changing the map size and panning the map moving interactively through speech. In the current system, the results of the queries are presented back to users through graphical user interface. An overview and major components of the MapPointS system will be presented in detail first. This will be followed by software design engineering principles and considerations adopted in developing the MapPointS system, and by a description of some key robust speech processing technologies underlying general speech-centric human-computer interaction systems.
A long-contextual-span model of resonance dynamics for speech recognition: parameter learning and recognizer evaluation

We present a structured speech model that is equipped with the capability of jointly representing incomplete articulation and long-span co-articulation in natural human speech. Central to this model is compact statistical parameterization of the highly regular dynamic patterns (exhibited in the hidden vocal-tract-resonance domain) that are driven by the stochastic segmental targets. We provide a rigorous mathematical description of this model, and present novel algorithms for learning the full set of model parameters using the cepstral data of speech. In particular, the gradient ascend techniques for learning variance parameters (for both resonance targets and cepstral prediction residuals) are described in detail. Phonetic recognition experiments are carried out using two paradigms of N-best rescoring and lattice search. Both sets of results demonstrate higher recognition accuracy achieved by the new model compared with the best HMM system. The higher accuracy is consistently observed, with and without combining HMM scores, and with and without including the references in the N-best lists and lattices. Further, the new model with rich parameter-free structure uses only the context-independent, single-modal Gaussian parameters, which are fewer than one percent of the parameters in the context-dependent HMM system with mixture distributions