Semi-automated Speaker Adaptation: How to Control the Quality of Adaptation?
Since the early 1990s, speaker adaptation have become one of the intensive areas in speech recognition. State-of-the-art batch-mode adaptation algorithms assume that speech of particular speaker contains enough information about the user's voice. In this article we propose to allow the user to manually verify if the adaptation is useful. Our procedure requires the speaker to pronounce syllables containing each vowel of particular language. The algorithm contains two steps looping through all syllables. At first, LPC analysis is performed for extracted vowel and the LPC coefficients are used to synthesize the new sound (with a fixed pitch period) and play it. If this synthesized sound is not perceived by the user as an original one then the syllable should be recorded again. At the second stage, speaker is asked to produce another syllable with the same vowel to automatically verify the stability of pronunciation. If two signals are closed (in terms of the Itakura-Saito divergence) then the sounds are marked as "good" for adaptation. Otherwise both steps are repeated. In the experiment we examine a problem of vowel recognition for Russian language in our voice control system which fuses two classifiers: the CMU Sphinx with speaker-independent acoustic model and Euclidean comparison of MFCC features of model vowel and input signal frames. Our results support the statement that the proposed approach provides better accuracy and reliability in comparison with traditional MAP/MLLR techniques implemented in the CMU Sphinx.
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We have recently introduced an irregularity index λ for daily sunspot numbers ISSN, derived from the well-known Lyapunov exponent, that attempts to reflect irregularities in the chaotic process of solar activity. Like the Lyapunov exponent, the irregularity index is computed from the data for different embedding dimensions m (2-32). When m = 2, λ maxima match ISSN maxima of the Schwabe cycle, whereas when m = 3, λ maxima occur at ISSN minima. The patterns of λ as a function of time remain similar from m = 4 to 16: the dynamics of λ change between 1915 and 1935, separating two regimes, one from 1850 to 1915 and the other from 1935 to 2005, in which λ retains a similar structure. A sharp peak occurs at the time of the ISSN minimum between cycles 23 and 24, possibly a precursor of unusual cycle 24 and maybe a new regime change. λ is significantly smaller during the ascending and descending phases of solar cycles. Differences in values of the irregularity index observed for different cycles reflect differences in correlations in sunspot series at a scale much less than the 4-yr sliding window used in computing them; the lifetime of sunspots provides a source of correlation at that time scale. The burst of short-term irregularity evidenced by the strong l-peak at the minimum of cycle 23-24 would reflect a decrease in correlation at the time scale of several days rather than a change in the shape of the cycle.
The influence of the length of the sample series of economic dynamics to the correct diagnosis of the structure of autoregressive models. It is proved that the length of the sample uvelichina further than the defined period of economic inertial object correlogram distort the real situation, and autoregression models are wrong structure. All scientific hypotheses tested on a representative sample of daily data in world oil prices over the past five years.
In this paper we consider the automatic emotions recognition problem, especially the case of digital audio signal processing. We consider and verify an approach in which the classification of a sound fragment is reduced to the problem of image recognition. The waveform and spectrogram are used as a visual representation of the image. The computational experiment was done based on Radvess open dataset including 8 different emotions: "neutral", "calm", "happy," "sad," "angry," "scared", "disgust", "surprised". The best accuracy result was 64%, which was produced by a combination of “|spectrogram + convolution neural network VGG-11”
The definition of a phoneme as a fuzzy set of minimal speech units from the model database is proposed. On the basis of this definition and the Kullback-Leibler minimum information discrimination principle the novel phoneme recognition algorithm has been developed as an enhancement of the phonetic decoding method. The experimental results in the problems of isolated vowels recognition and word recognition in Russian are presented. It is shown that the proposed method is characterized by the increase of recognition accuracy and reliability in comparison with the phonetic decoding method
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The geographic information system (GIS) is based on the first and only Russian Imperial Census of 1897 and the First All-Union Census of the Soviet Union of 1926. The GIS features vector data (shapefiles) of allprovinces of the two states. For the 1897 census, there is information about linguistic, religious, and social estate groups. The part based on the 1926 census features nationality. Both shapefiles include information on gender, rural and urban population. The GIS allows for producing any necessary maps for individual studies of the period which require the administrative boundaries and demographic information.
It is well-known that the class of sets that can be computed by polynomial size circuits is equal to the class of sets that are polynomial time reducible to a sparse set. It is widely believed, but unfortunately up to now unproven, that there are sets in EXPNP, or even in EXP that are not computable by polynomial size circuits and hence are not reducible to a sparse set. In this paper we study this question in a more restricted setting: what is the computational complexity of sparse sets that are selfreducible? It follows from earlier work of Lozano and Torán (in: Mathematical systems theory, 1991) that EXPNP does not have sparse selfreducible hard sets. We define a natural version of selfreduction, tree-selfreducibility, and show that NEXP does not have sparse tree-selfreducible hard sets. We also construct an oracle relative to which all of EXP is reducible to a sparse tree-selfreducible set. These lower bounds are corollaries of more general results about the computational complexity of sparse sets that are selfreducible, and can be interpreted as super-polynomial circuit lower bounds for NEXP.