nils-herrmann/clean_scirepeval huggingface dataset:
Prosody dependent speech recognition with explicit duration modelling at intonational phrase boundaries

Does prosody help word recognition? In this paper, we propose a novel probabilistic framework in which word and phoneme are dependent on prosody in a way that improves word recognition. The prosody attribute that we investigate in this study is the duration lengthening effects of the speech segments in the vicinity of intonational phrase boundaries. Explicit Duration Hidden Markov Model (EDHMM) is implemented to provide an accurate phoneme duration model. This study is conducted on Boston University Radio New Corpus with prosodic boundaries marked using ToBI labelling system. We found that lengthening of the phrase final rhymes can be reliably modelled by EDHMM, which significantly improves the prosody dependent acoustic modelling. Conversely, no systematic duration variation is found at phrase initial position. With prosody dependence implemented in acoustic model, pronunciation model and language model, both word recognition accuracy and boundary recognition accuracy are improved by 1% over systems without prosody dependence.
Non-linear maximum likelihood feature transformation for speech recognition

Most automatic speech recognition systems (ASR) use Hidden Markov model (HMM) with a diagonal-covariance Gaussian mixture model for the state-conditional probability density function. The diagonal-covariance Gaussian mixture can model discrete sources of variability like speaker variations, gender variations, or local dialect, but can not model continuous types of variability that account for correlation between the elements of the feature vector. In this paper, we present a transformation of the acoustic feature vector that minimize an empirical estimate of the relative entropy between the likelihood based on the diagonal-covriance Gaussian mixture HMM model and the true likelihood. We show that this minimization is equivalent to maximizing the likelihood in the original feature space. Based on this formulation, we provide a computationally efficient solution to the problem based on volume-preserving maps; existing linear feature transform designs are shown to be special cases of the proposed solution. Since most of the acoustic features used in ASR are not linear functions of the sources of correlation in the speech signal, we use a non-linear transformation of the features to minimize this objective function. We describe an iterative algorithm to estimate the parameters of both the volume-preserving feature transformation and the hidden Markov models (HMM) that jointly optimize the objective function for an HMM-based speech recognizer. Using this algorithm, we achieved 2% improvement in phoneme recognition accuracy compared to the original system that uses the original Mel-frequency cepstral coeeficients (MFCC) acoustic features. Our approach is compared also to previous similar linear approaches like MLLT and ICA.
Improving the robustness of prosody dependent language modeling based on prosody syntax dependence

The paper presents a novel approach that improves the robustness of prosody dependent language modeling by leveraging the dependence between prosody and syntax. A prosody dependent language model describes the joint probability distribution of concurrent word and prosody sequences and can be used to provide prior language constraints in a prosody dependent speech recognizer. Robust maximum likelihood (ML) estimation of prosody dependent n-gram language models requires a large amount of prosodically transcribed data. We show that prosody-syntax dependence can be utilized to diminish the data sparseness introduced by prosody dependent modeling. Experiments on a radio news corpus show that the prosody dependent language model estimated using our approach reduces the joint perplexity by up to 34% as compared with the standard ML-estimated prosody dependent language model; the word perplexity can be reduced by up to 84% as compared with the standard ML-estimated prosody independent language model. In recognition experiments, the language model estimated by our approach create an improvement of 1% in word recognition accuracy, 0.7% in accent recognition accuracy and 1.5% in intonational phrase boundary (IPB) recognition accuracy over a baseline prosody dependent language model.
An evaluation of using mutual information for selection of acoustic-features representation of phonemes for speech recognition

This paper addresses the problem of finding a subset of the acoustic feature space that best represents the phoneme set used in a speech recognition system. A maximum mutual information approach is presented for selecting acoustic features to be combined together to represent the distinctions among the phonemes. The overall phoneme recognition accuracy is slightly increased for the same length of feature vector for clean speech and at 10 dB compared to FFT-based Mel-frequency cepstrum coefficients (MFCC) by using acoustic features selected based on a maximum mutual information criterion. Using 16 different feature sets, the rank of the feature sets based on mutual information can predict phoneme recognition accuracy with a correlation coefficient of 0.71 compared to a correlation coefficient of 0.28 when using a criterion based on the average pair-wise Kullback-Liebler divergence to rank the feature sets.
Maximum mutual information based acoustic-features representation of phonological features for speech recognition

This paper addresses the problem of finding a subset of the acoustic feature space that best represents a set of phonological features. A maximum mutual information approach is presented for selecting acoustic features to be combined together to represent the distinctions coded by a set of correlated phonological features. Each set of phonological features is chosen on the basis of acoustic phonetic similarity, so the sets can be considered approximately independent. This means that the output of recognizers that recognize these sets independently using the acoustic representation achieved by an algorithm presented in this paper can be combined together to increase efficiency and robustness of speech recognition systems. The mutual information between the phonological feature sets and their achieved acoustic representation is increased by up to 220% over the best single-type acoustic representation in the feature space of the same length.
Auditory-modeling inspired methods of feature extraction for robust automatic speech recognition

This paper proposes a technique of extracting robust feature vectors for ASR. The technique is inspired by work related to auditory modeling. It involves first filtering the speech signal through a bank of band-pass filters, which are based on a model of the human cochlea. Autocorrelation functions (ACF) are computed on the filters' outputs. Then the individual ACFs are scaled by their corresponding voice indices (VIs), which use information related to the pitch. A summed ACF is then obtained by summing the individual ACFs across the bands. Feature vectors are then computed using standard cepstral analysis, by treating the summed ACF as a regular ACF. Finally, frame indices (FIs) weigh the feature vectors in the time domain. The effectiveness of the proposed techniques, compared to LPCC and MFCC, are demonstrated by comparing the results obtained from simple recognition experiments.
PLP coefficients can be quantized at 400 bps

Previous work in wireless speech recognition has focused on two methods, namely, quantizing recognition features (e.g. MFCC) or performing recognition using speech coding parameters (e.g. LPC). All of this previous research assumes that the communication channel is only large enough to transmit either speech coding parameters or speech recognition parameters. By contrast, we propose that the speech recognition parameters can be quantized at a rate sufficiently low to allow transmission of both speech coding and speech recognition parameters over a standard cellular channel. In particular, the paper shows that the perceptual LPC (PLP) coefficients can be transmitted at 400 bps with an insignificant loss of digit recognition accuracy.
Gaussian mixture models of phonetic boundaries for speech recognition

A new approach to represent temporal correlation in an automatic speech recognition system is described. It introduces an acoustic feature set that captures the dynamics of a speech signal at the phoneme boundaries in combination with the traditional acoustic feature set representing the periods that are assumed to be quasi-stationary of speech. This newly introduced feature set represents an observed random vector associated with the state transition in HMM. For the same complexity and number of parameters, this approach improves the phoneme recognition accuracy by 3.5% compared to the context-independent HMM models. Stop consonant recognition accuracy is increased by 40%.
Acoustics, Audio, and Music Technology Education at the University of Illinois at Urbana‐Champaign

Several courses combine to form an unofficial but nevertheless effective undergraduate and early graduate music technology program at the University of Illinois at Urbana–Champaign. Musical Acoustics is taught within the School of Music and includes topics such as resonance behavior of strings and pipes; perception of loudness, pitch, and timbre; and acoustics of musical instruments. Several courses involving computers and music are taught in the School of Music. These courses discuss the fundamentals of computer music, and extensive computer/recording studios provide students with hands‐on opportunities to conduct experiments or to produce audio demonstrations or music compositions. The courses differ in terms of the user interfaces, compositional algorithms, and synthesis models employed. There is also a seminar in analysis and synthesis of sounds. In the Department of Electrical and Computer Engineering, besides conventional digital signal processing, there are several special courses that offer traini...
Multivariate-state hidden Markov models for simultaneous transcription of phones and formants

A multivariate-state HMM-an HMM with a vector state variable-can be used to find jointly optimal phonetic and formant transcriptions of an utterance. The complexity of searching a multivariate state space using the Baum-Welch algorithm is substantial, but may be significantly reduced if the formant frequencies are assumed to be conditionally independent given knowledge of the phone. Operating with a known phonetic transcription, the multivariate-state model can provide a maximum a posteriori formant trajectory, complete with confidence limits on each of the formant frequency measurements. The model can also be used as a phonetic classifier by adding the probabilities of all possible formant trajectories. A test system is described which requires only nine trainable parameters per formant per phonetic state: five parameters to model formant transitions, and four to model spectral observations. Further simplifications were achieved through parameter tying.
Line spectral frequencies are poles and zeros of the glottal driving-point impedance of a discrete matched-impedance vocal tract model.

This correspondence demonstrates that the line spectral frequencies (LSFs) are the pole and zero frequencies of the glottal driving-point impedance of a discrete matched-impedance vocal-tract model. Several well-known characteristics of the LSFs, including the interlacing of pole and zero frequencies, are shown to follow naturally from this proof.
Signal approximation in Hilbert space and its application on articulatory speech synthesis

In this paper, we apply signal approximation theory to estimate the articulatory trajectory for a nonlinear speech synthesizer. First, we analyze the L2 error bounds of interpolation and LS approximation in the Hilbert space framework. Second, we use two signal approximation techniques, speci cally, cubic spline interpolation and the LS approximation to estimate the trajectory of articulatory parameters given the static articulatory parameters of discrete phonemes. In our articulatory speech synthesizer, we assume that the sound propagation inside the human vocal tract is a three-dimensional non-plane wave propagation inside a viscous uid described by the governing Navier-Stoke equations. Finally, we present some experimental results of the estimated articulatory trajectories of English diphthongs and the synthesized phonemes.
Time-frequency distribution of partial phonetic information measured using mutual information

This paper uses the method of mutual information to estimate the distribution of partial phonetic information in the time-frequency plane relative to an acoustic landmark. TIMIT transcriptions are parsed to estimate the locations of consonant closure landmarks, consonant release landmarks, manner change landmarks, and vowel or glide pivot landmarks. A mel-scale spectrogram is computed over the 250ms centered at each landmark, and the logarithmic energy of each point in time-frequency space is linearly quantized. The phoneme label associated with a landmark determines the values of 25 binary distinctive features. Finally, coincidences between feature and spectral energy values are counted, and the average log probabilities are calculated in order to produce an \infogram" of each distinctive feature: a measurement of the mutual information between the value of the feature and the energy of each point in the time-frequency plane.
CTMRedit : A Case Study in Human-Computer Interface Design

CTMRedit (affectionately known as MRCAT, or 'Mr. Cat”) is a software tool for displaying, editing, and three-dimensional reconstruction of MR and CT images. This paper describes the incremental evolution of MRCAT during a period in which it was used regularly by the two software designers, and by two medical experts naive with respect to the software design. Three methods were found to be useful for suggesting changes to the software: use of the software, and conversations with other users, were helpful for identifying useful new functionality, while direct observation of naive users was most useful for identifying problems with the existing user interface. As expected, naive users are generally most efficient when presented with a familiar-looking user interface, e.g. radiologists are familiar with film, so images on screen should be smoothly interpolated so that they look like film. An unexpected finding is that not all of the software implementation details should be hidden. Implementation details which the user must know, e.g. organization of an image database, should be displayed prominently in the user interface, so that they will quickly become familiar to the user.
CTMRedit: a Matlab-based tool for segmenting and interpolating MRI and CT images in three orthogonal planes

CTMRedit is a GUI tool for viewing CT and MRI images in three orthogonal planes, for manually or automatically segmenting regions of interest (ROI), and for interpolating between outline contours to create a 3D outline surface. Features of CTMRedit include display and I/O of 8-bit and 16-bit image formats, easy navigation of a large image database, and easy-to-use ROI editing tools. CTMRedit is written in the Matlab programming language, so the code is platform-independent and easy to extend or re-use.
Electromagnetic exposure safety of the Carstens articulograph AG100.

Extremely strong magnetic fields at the frequencies used in an electromagnetic articulometer (EMA) system may pose a risk to the health of a subject. To avoid such risks, the field strengths produced by any EMA system should be measured, and compared to published permissible exposure standards. This Letter reports measurements of the 2–50 kHz magnetic field strength of the Carstens Articulograph AG100. The measured field strength (35 μT at a distance of 7.5 cm) is permissible under local standards in Austria, Germany, the UK, and the USA (except Massachusetts), but may be impermissible under the 1995 European Community Prestandard, and in Canada.
Information theory and variance estimation techniques in the analysis of category rating and paired comparisons

Speech clarity judgments of bandpass filtered sentences were measured by category rating and paired comparisons in five age groups—4, 5, 6, 7, and 8 years old, and adults [data for children were provided from Eisenberg and Dirks,  (1995)]. The judgment data within each age group were subjected to information theory and variance estimation analytical techniques in order to investigate developmental differences in task performance capability. All response variability was converted into equivalent signal detection ‘‘error’’ components, representing the ability of subjects in a given age group to perform a given psychophysical procedure. Preliminary results revealed a different developmental trend for each psychophysical procedure. For category rating, children 4 to 6 years of age exhibited higher error components in their responses, suggesting reduced capability to extract information compared with adults. In contrast, children as young as 5 years of age were approaching a...
Burst spectral measures and formant frequencies can be used to accurately discriminate place of articulation

Given an accurate burst location, four automatic measurements of the burst spectral shape and three semiautomatic formant onset measurements were used to correctly classify place of articulation of 90% of a corpus of 124 stops. At least 20 releases of each of the six English stops, in unconstrained prevocalic, preglide, or (for velar and labial stops only) pre‐/r/ context, were chosen at random from the TIMIT corpus. Burst locations were marked by hand. The values of F2 and F3 20 ms later, and of ΔF2 between 20 and 50 ms later, were measured automatically, and 18% of the tokens were corrected by hand. Burst compactness, peak frequency, and mid‐ and high‐frequency amplitude were measured automatically. In separate analyses of variance, all seven measures showed significant place effects (p<0.001). In three pairwise linear discriminants, the burst measures alone were able to classify place with 85% accuracy, showing that automatic measurements can reproduce the result of Blumstein and Stevens [
Robust Speech Feature Extraction by Growth Transformation in Reproducing Kernel Hilbert Space

The performance of speech recognition systems depends on consistent quality of the speech features across variable environmental conditions encountered during training and evaluation. This paper presents a kernel-based nonlinear predictive coding procedure that yields speech features which are robust to nonstationary noise contaminating the speech signal. Features maximally insensitive to additive noise are obtained by growth transformation of regression functions that span a reproducing kernel Hilbert space (RKHS). The features are normalized by construction and extract information pertaining to higher-order statistical correlations in the speech signal. Experiments with the TI-DIGIT database demonstrate consistent robustness to noise of varying statistics, yielding significant improvements in digit recognition accuracy over identical models trained using Mel-scale cepstral features and evaluated at noise levels between 0 and 30-dB signal-to-noise ratio.
Design and implementation of ultra-low power pattern and sequence decoders

A key challenge in embedding pattern recognition intelligence onto ubiquitous sensing and communication interfaces in wireless integrated systems is to balance requirements on precision, complexity and power consumption in VLSI implementation. This dissertation investigates architectures for adaptive pattern recognition and sequence decoding, derived from statistical learning theory and Bayesian belief propagation on graphs, that lend naturally to efficient implementation in analog VLSI. Theoretical research in this area has resulted in forward decoding kernel machines (FDKM), a maximum a posteriori (MAP) based sequence decoder and has demonstrated state-of-art performance on various signal processing tasks in speech recognition and communications. The performance of FDKM depends on the discriminatory ability of an embedded large margin classifier. Investigation in this area has led to development of Gini-support vector machines, a sparse large margin classifier generating normalized output scores. These have been used extensively for image classification and voting networks. GiniSVM and FDKM systems have been optimized for accuracy and power dissipation and their architecture have been mapped onto a current-mode CMOS implementation. Non-volatile floating-gate MOS storage provides full analog programmability and trainability throughout all stages of the architecture. A calibration scheme, coupled with a chip-in-the-loop retraining procedure, cancels imprecision due to fabrication-induced mismatch in the analog circuit implementation. Calibration and retraining of a 3mm x 3mm SVM/FDKM chip fabricated in 0.5um CMOS technology, programmed for a speaker verification task, yields real-time recognition accuracy on par with floating-point software while dissipating sub-microwatt power. Applications include power-efficient smart sensors, implantable biomedical monitoring, biometric verification, human-computer interfaces, and adaptive iterative decoding communication systems.
A dynamic reconfigurable A/D converter for sensor applications

A hybrid algorithmic-SigmaDelta ADC has been developed for sensor applications that benefit from dynamic reconfiguration of the tradeoff between resolution and conversion speed. By iteratively feeding back and resampling the residue of a SigmaDelta conversion, bit weight information is embedded into the digital output sequence as in an algorithmic conversion. By varying the number of sampling and feedback cycles, the ADC can be dynamically reconfigured at the architectural level to be more SigmaDelta-like, achieving higher resolution with lower speed, or more algorithmic-like, providing higher speed with lower resolution. With a nominal 10MHz clock, the ADC can resolve 8 bits in 1.6musec, 16 bits in 51.2musec or various configurations in between. Analysis of an example sensor application shows a 78% power savings due to dynamic reconfiguration to match signal characteristics
Fixed-current method for programming large floating-gate arrays

The speed and accuracy of programming large floating gate arrays is limited by precision of current measurement and pulse generator. In this paper a novel floating gate programming method is proposed which alleviates the requirement of accurate measurement and facilitates fast programming. As opposed to conventional approaches this method programs fixed value of currents onto the floating gate transistor at variable control gate voltage. The technique is used in conjunction with a coarse programming method, that utilizes the positive feedback nature of pFET injection to improve the speed of programming. Measured results from an array of floating gate cells, fabricated in a 0.5 /spl mu/m CMOS process demonstrate the effectiveness of this method to program currents in the order of few picoamperes with approximately 8 bits of resolution.