nils-herrmann/clean_scirepeval huggingface dataset:
Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology

PHiPAC was an early attempt to improve software performance by searching in a large design space of possible implementations to find the best one. At the time, in the early 1990s, the most efficient numerical linear algebra libraries were carefully hand tuned for specific microarchitectures and compilers, and were often written in assembly language. This allowed very precise tuning of an algorithm to the specifics of a current platform, and provided great opportunity for high efficiency. The prevailing thought at the time was that such an approach was necessary to produce near-peak performance. On the other hand, this approach was brittle, and required great human effort to try each code variant, and so only a tiny subset of the possible code design points could be explored. Worse, given the combined complexities of the compiler and microarchitecture, it was difficult to predict which code variants would be worth the implementation effort. PHiPAC circumvented this effort by using code generators that could easily generate a vast assortment of very different points within a design space, and even across very different design spaces altogether. By following a set of carefully crafted coding guidelines, the generated code was reasonably efficient for any point in the design space. To search the design space, PHiPAC took a rather naive but effective approach. Due to the human-designed and deterministic nature of computing systems, one might reasonably think that smart modeling of the microprocessor and compiler would be sufficient to predict, without performing any timing, the optimal point for a given algorithm. But the combination of an optimizing compiler and a dynamically scheduled mi-
Representing groove: Rhythmic structure in interactive music performance

The concept of ‘‘groove,’’ a musical element particularly operative in African‐derived musics such as jazz, may be described as a collectively determined and relatively isochronous pulse that can be inferred from the interaction of a number of interlocking rhythmic groups. A powerful representation for musical rhythm as it appears in groove‐oriented contexts has been developed. Implemented in the MAX music‐programming environment, the representation encompasses pitch, accent, rhythmic deviations, tempo variation, note durations, and probabilistic processes. The design facilitates ‘‘bottom‐up’’ combination techniques such as the construction of large musical objects by assembling small musical ‘‘cells’’ in series or parallel, and thus it allows improvisatory manipulation of simple rhythmic structures. The richness of control over many meaningful musical quantities distinguishes the representation from those in more common usage, such as music notation programs, sequencers, and drum machines. The implementa...
Using PHiPAC to speed error back-propagation learning

We introduce PHiPAC, a coding methodology for developing portable high-performance numerical libraries in ANSI C. Using this methodology, we have developed code for optimized matrix multiply routines. These routines can achieve over 90% of peak performance on a variety of current workstations, and are often faster than vendor-supplied optimized libraries. We then describe the bunch-mode back-propagation algorithm and how it can use the PHiPAC derived matrix multiply routines. Using a set of plots, we investigate the tradeoffs between bunch size, convergence rate, and training speed using a standard speech recognition data set and show how use of the PHiPAC routines can lead to a significantly faster back-propagation learning algorithm.
A novel representation for rhythmic structure

We have developed a representation for musical rhythm and rhythmic structure based on concepts derived from African and African-American musics. Included in the representation are a model for expressive timing against an isochronous pulse, and a cellular approach to musical organization. In our implementation, the representation and its data structures are controlled and modified in real time using MAX. The richness of control over many meaningful musical quantities distinguishes our representation from those in more common usage, such as music notation programs, sequencers, and drum machines.
Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology

Modern microprocessors can achieve high performance on linear algebra kernels but this currently requires extensive machinoapeci6c hand tuning. We have developed a methodology whereby near-peak performance on a wide range of systems can be achieved automatically for such routines. First, by analyzing current machines and C compilers, we’ve developed guidelines for writing Portable, High-Performance, ANSI C (PHiPAC, pre nounced Weopack”). Second, rather than code by hand, we produce parameterized code generators. Third, we write search scripts that find the best parameters for a given system. We report on a BLAS GEMM compatible multi-level cache-blocked matrix multiply generator which produces code that achieves around 90% of peak on the Sparcstation-20/61, IBM RS/6000-590, HP 712/8Oi, SGI Power Challenge RBk, and SGI Octane RlOk, and over 80% of peak on the SGI Indigo R4k. The resulting routines are competitive with vendoroptimized BLAS GEMMs. l CS Divirion, University of California at Berkeley and the International Computer Science Inatitute. The author ncknowledgea the support of JSBP contract F49620-94COOSS. ‘CS Divirion, University of California at Berkeley and the International Computer Science Inrtitute. The author acknowledger the support of ONR UBJ Grant NOO01492-J-1617. ‘CS Divirion, Univenity of California at Berkeley. The author acknowledgea the rupport of ARPA contract DAALOSQIC-0047 (University of ‘Ibnneaaee Subcontract OBA4466.02). tCS Division and Mathematia Dept., University of California at Berkeley. The author acknowledger the rupport of ARPA contract DAAL0991-C-0047 (Univenity of Tenneaaee Subcontract OBA4466.02). ARPA contract DAAH04961-0077 (University of Tennea&Subcontract OB.A7463.02), DOE grant‘ DE PG0394ER26219, DOE contract W-31-109-Eng-33, NSF grant ASC-QSlSQSS, and DOE grant DEFG03-94ER26206. Permission to make digitahbard copies of all or pntt of this material for personal or classrocm use is mted without fee pmvided that the copies are not made or distributed for profit or commercial advantage, the copy right notice. the title ofthe publication and its date appear. and notice is given that copyigbht is by petmission of the ACM, Inc. To copy otherwise. to republish. to post on servers or to r&tribute to lists. requires specific permission and/or fee ICY 97 Vienna Austria Copyright 1997 ACM O-8979l-902-5/97ff..SB.50
Joint distributional modeling with cross-correlation based features

In maximum likelihood based speech recognition systems, it is important to accurately estimate the joint distribution of feature vectors given a particular acoustic model. We propose that by modeling the joint distribution of time localized feature vectors and statistics relating those time localized feature vectors to the relevant acoustic context, we can estimate information contained in the feature vector joint distribution without the accompanying theoretical or computational difficulties. We introduce the modcrossgram (MCG), a computational way of estimating short time spectro temporal correlation based statistics that are informative about the feature vector joint distribution. Using the standard hybrid ANN/HMM architecture, we compare a MCG based speech recognition system with a more traditional one on an isolated word speech database. We show that, in the presence of noise, the MCG based system achieves a significant reduction in word error rate over the standard system.
Stochastic perceptual speech models with durational dependence

In (Morgan et al., 1994), we developed a statistical model of speech recognition where emphasis was placed on the perceptually-relevant and information-rich portion of the speech signal. In that model, speech is viewed as a sequence of elementary decisions or auditory events (avents) that are made in response to loci of significant spectral change. These decision points are interleaved with periods during which insufficient information has been accumulated to make the next decision. We have called this a stochastic perceptual avent model, or SPAM. In the work reported, we have extended our initial experimental implementation to include other probabilistic dependencies specified in the original theory, particularly the dependence on the time from the current frame back to the previous hypothesized avent.
Subjective evaluation of join cost and smoothing methods for unit selection speech synthesis

In unit selection-based concatenative speech synthesis, join cost (also known as concatenation cost), which measures how well two units can be joined together, is one of the main criteria for selecting appropriate units from the inventory. Usually, some form of local parameter smoothing is also needed to disguise the remaining discontinuities. This paper presents a subjective evaluation of three join cost functions and three smoothing methods. We also describe the design and performance of a listening test. The three join cost functions were taken from our previous study, where we proposed join cost functions derived from spectral distances, which have good correlations with perceptual scores obtained for a range of concatenation discontinuities. This evaluation allows us to further validate their ability to predict concatenation discontinuities. The units for synthesis stimuli are obtained from a state-of-the-art unit selection text-to-speech system: rVoice from Rhetorical Systems Ltd. In this paper, we report listeners' preferences for each join cost in combination with each smoothing method
Multidimensional scaling of listener responses to synthetic speech

The move to unit-selection in speech synthesis has resulted in system improvements being made at subtle suband suprasegmental levels. Human perceptual evaluation of such subtle improvements requires a highly sophisticated level of perceptual attention to specific acoustic characteristics or cues. However, it is not well understood what acoustic cues listeners attend to by default when asked to evaluate synthetic speech. It may, therefore, be potentially quite difficult to design an evaluation method that allows listeners to concentrate on only one dimension of the signal, while ignoring others that are perceptually more important to them. This paper describes a pilot study which aims to evaluate multidimensional scaling (MDS) as a possible method of determining what acoustic characteristics of synthetic speech influence listeners’ judgements of the naturalness of the speech. Using distance measures (either real or perceived distances), MDS techniques represent stimuli as points in n-dimensional space. The space is configured so that similar stimuli are close together, while different stimuli are farther apart. Additionally, the dimensions of the space correspond to characteristics of the stimuli which influenced the perceived distances. Our results indicate that MDS techniques should be a useful tool in understanding the complex psychoacoustic processes that listeners undergo when evaluating synthetic speech. This method has allowed us to identify a number of cues that appear to be particularly perceptually salient to listeners evaluating synthetic speech naturalness, namely prosodic cues (in terms of duration and/or intonation) and segmental or unit level cues (in terms of appropriateness of units, or number of units).
A hybrid ANN/DBN approach to articulatory feature recognition

Artificial neural networks (ANN) have proven to be well suited to the task of articulatory feature (AF) recognition. Previous studies have taken a cascaded approach where separate ANNs are trained for each feature group, making the assumption that features are statistically independent. We address this by using ANNs to provide virtual evidence to a dynamic Bayesian network (DBN). This gives a hybrid ANN/DBN model and allows modelling of inter-feature dependencies. We demonstrate significant increases in AF recognition accuracy from modelling dependencies between features, and present the results of embedded training experiments in which a set of asynchronous feature changes are learned. Furthermore, we report on the application of a Viterbi training scheme in which we alternate between realigning the AF training labels and retraining the ANNs.
Detection of symbolic gestural events in articulatory data for use in structural representations of continuous speech

One of the crucial issues which often needs to be addressed in structural approaches to speech representation is the choice of fundamental symbolic units of representation. In this paper, a physiologically inspired methodology for defining these symbolic atomic units in terms of primitive articulatory events is proposed. It is shown how the atomic articulatory events (gestures) can be detected directly in the articulatory data. An algorithm for evaluating the reliability of the articulatory events is described and promising results of the experiments conducted on the MOCHA articulatory database are presented.
Predicting consonant duration with Bayesian belief networks

Consonant duration is influenced by a number of linguistic factors such as the consonant’s identity, within-word position, stress level of the previous and following vowels, phrasal position of the word containing the target consonant, its syllabic position, identity of the previous and following segments. In our work, consonant duration is predicted from a Bayesian belief network (BN) consisting of discrete nodes for the linguistic factors and a single continuous node for the consonant’s duration. Interactions between factors are represented as conditional dependency arcs in this graphical model. Given the parameters of the belief network, the duration of each consonant in the test set is then predicted as the value with the maximum probability. We compare the results of the belief network model with those of sums-of-products (SoP) and classification and regression tree (CART) models using the same data. In terms of RMS error, our BN model performs better than both CART and SoP models. In terms of the correlation coefficient, our BN model performs better than SoP model, and no worse than CART model. In addition, the Bayesian model reliably predicts consonant duration in cases of missing or hidden linguistic factors.
Inductive String Template-Based Learning of Spoken Language

This paper deals with formulation of alternative structural approach to the speech recognition problem. In this approach, we require both the representation and the learning algorithms defined on it to be linguistically meaningful, which allows the speech recognition system to discover the nature of the linguistic classes of speech patterns corresponding to the speech waveforms. We briefly discuss the current formalisms and propose an alternative — a phonologically inspired string-based inductive speech representation, defined within an analytical framework specifically designed to address the issues of class and object representation. We also present the results of the phoneme classification experiments conducted on the TIMIT corpus of continuous speech.
Speech Synthesis in the Gorbals Region of Glasgow Alexis Grant

This report attempts to determine whether improving the phonetic match between a lexicon used in speech synthesis and the speech of the speaker who provides the source for a synthetic voice improves the quality and authenticity of the synthetic voice. In order to explore this question, a survey is made of the literature describing the accent of urban Glasgow, and these data serve as the basis for the accent’s implementation in the Unisyn accent-independent lexicon system. After implementation, two voices are built from the accent, one using an automatic labeling procedure and one using a corrected labeling. The voices are assessed subjectively for quality and compared in forced-choice listening tests to reference voices built using the Edinburgh accent of the Unisyn lexicon. The results are inconclusive, but the method is generalizable and potentially powerful.
Subjective evaluation of join cost functions used in unit selection speech synthesis

In our previous papers, we have proposed join cost functions derived from spectral distances, which have good correlations with perceptual scores obtained for a range of concatenation discontinuities. To further validate their ability to predict concatenation discontinuities, we have chosen the best three spectral distances and evaluated them subjectively in a listening test. The unit sequences for synthesis stimuli are obtained from a state-of-the-art unit selection text-to-speech system: rVoice from Rhetorical Systems Ltd. In this paper, we report listeners' preferences for each of the three join cost functions.
Phone classification in pseudo-euclidean vector spaces

Recently we have proposed a structural framework for modelling speech, which is based on patterns of phonological distinctive features, a linguistically well-motivated alternative to standard vector-space acoustic models like HMMs. This framework gives considerable representational freedom by working with features that have explicit linguistic interpretation, but at the expense of the ability to apply the wide range of analytical decision algorithms available in vector spaces, restricting oneself to more computationally expensive and less-developed symbolic metric tools. In this paper we show that a dissimilarity-based distance-preserving transition from the original structural representation to a corresponding pseudo-Euclidean vector space is possible. Promising results of phone classification experiments conducted on the TIMIT database are reported.
Estimating Detailed Spectral Envelopes Using Articulatory Clustering Yoshinori Shiga

This paper presents an articulatory-acoustic mapping where detailed spectral envelopes are estimated. During the estimation, the harmonics of a range of values are derived from the spectra of multiple voiced speech signals vocalized with similar articulator settings. The envelope formed by these harmonics is represented by a cepstrum, which is computed by fitting the peaks of all the harmonics based on the weighted least square method in the frequency domain. The experimental result shows that the spectral envelopes are estimated with the highest accuracy when the cepstral order is 48-64 for a female speaker, which suggests that representing the real response of the vocal tract requires high-quefrency elements that conventional speech synthesis methods are forced to discard in order to eliminate the pitch component of speech.
Source-filter separation for articulation-to-speech synthesis

In this paper we examine a method for separating out the vocal-tract filter response from the voice source characteristic using a large articulatory database. The method realises such separation for voiced speech using an iterative approximation procedure under the assumption that the speech production process is a linear system composed of a voice source and a vocal-tract filter, and that each of the components is controlled independently by different sets of factors. Experimental results show that the spectral variation is evidently influenced by the fundamental frequency or the power of speech, and that the tendency of the variation may be related closely to speaker identity. The method enables independent control over the voice source characteristic in our articulation-to-speech synthesis.
Subjective evaluation of join cost & smoothing methods

In our previous papers, we have proposed join cost functions derived from spectral distances, which have good correlations with perceptual scores obtained for a range of concatenation discontinuities. To further validate their ability to predict concatenation discontinuities, we have chosen the best three spectral distances and evaluated them subjectively in a listening test. The units for synthesis stimuli are obtained from a state-of-the-art unit selection text-to-speech system: rVoice from Rhetorical Systems Ltd. We also compared three different smoothing methods in this listening test. In this paper, we report listeners’ preferences for each join costs in combination with each smoothing method.
Accurate spectral envelope estimation for articulation-to-speech synthesis

This paper introduces a novel articulatory-acoustic mapping in which detailed spectral envelopes are estimated based on the cepstrum, inclusive of the high-quefrency elements which are discarded in conventional speech synthesis to eliminate the pitch component of speech. For this estimation, the method deals with the harmonics of multiple voiced-speech spectra so that several sets of harmonics can be obtained at various pitch frequencies to form a spectral envelope. The experimental result shows that the method estimates spectral envelopes with the highest accuracy when the cepstral order is 48-64, which suggests that the higherorder coefficients are required to represent detailed envelopes reflecting the real vocal-tract responses.
Estimating detailed spectral envelopes using articulatory clustering

This paper presents an articulatory-acoustic mapping where detailed spectral envelopes are estimated. During the estimation, the harmonics of a range of F0 values are derived from the spectra of multiple voiced speech signals vocalized with similar articulator settings. The envelope formed by these harmonics is represented by a cepstrum, which is computed by fitting the peaks of all the harmonics based on the weighted least square method in the frequency domain. The experimental result shows that the spectral envelopes are estimated with the highest accuracy when the cepstral order is 48-64 for a female speaker, which suggests that representing the real response of the vocal tract requires high-quefrency elements that conventional speech synthesis methods are forced to discard in order to eliminate the pitch component of speech.
8th ISCA Workshop on Speech Synthesis

s 10 Keynote Session 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Oral Session 1: Prosody. . . . . . . . . . . . . . . . . . . . . . . . . 12 Poster Session 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Keynote Session 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Oral Session 2: Deep  Analysis and Modeling for Speech Synthesis . . . .
Asynchronous Articulatory Feature Recognition Using Dynamic Bayesian Networks

This paper builds on previous work where dynamic Bayesian networks (DBN) were proposed as a model for articulatory feature recognition. Using DBNs makes it possible to model the dependencies between features, an addition to previous approaches which was found to improve feature recognition performance. The DBN results were promising, giving close to the accuracy of artificial neural nets (ANNs). However, the system was trained on canonical labels, leading to an overly strong set of constraints on feature co-occurrence. In this study, we describe an embedded training scheme which learns a set of data-driven asynchronous feature changes where supported in the data. Using a subset of the OGI Numbers corpus, we describe articulatory feature recognition experiments using both canonically-trained and asynchronous-feature DBNs. Performance using DBNs is found to exceed that of ANNs trained on an identical task, giving a higher recognition accuracy. Furthermore, inter-feature dependencies result in a more structured model, giving rise to fewer feature combinations in the recognition output. In addition to an empirical evaluation of this modeling approach, we give a qualitative analysis, investigating the asynchrony found through our data-driven method and interpreting it using linguistic knowledge.