Propagandos atpažinimas lietuviškame tekste naudojant transformeriais pagrįstus, iš anksto apmokytus daugiakalbius modelius

Didėjant informacijos kiekiui ir jos svarbai visuomenėje atsiranda vis didesnis poreikis automatinių įrankių, gebančių atpažinti propagandą. Dėl geopolitinės situacijos Lietuvos valstybė gali būti ypatingai pažeidžiama propagandinių mechanizmų, o automatinis jos atpažinimas lietuviškuose tekstuose yra nepakankamai ištyrinėta sritis. Šio darbo tikslas – išbandyti 3 pagrindinius transformeriais pagrįstus, iš anksto apmokytus daugiakalbius modelius propagandos atpažinimui. Sprendžiamas binarinis klasifikavimo uždavinys, priskiriant tekstui propagandinio arba nepropagandinio teksto klasę. LitLat, XLM-R ir mBERT modeliai adaptuoti apmokant ekspertų suanotuotu duomenų rinkiniu. Nors geriausią, 88,5 % F1 statistikos įvertį pavyko pasiekti adaptavus LitLat iš anksto apmokytą modelį, kiti šiame darbe adaptuoti modeliai pasiekia panašius rezultatus

Draudimo sektoriaus klientų atsiliepimų ir vertinimų nuotaikų kaitos analizė laike

Šiandien internetas tampa nepakeičiamas informacijos šaltinis, kuriame gausu įvairių atsiliepimų apie įsigytus produktus ar paslaugas. Šie atsiliepimai teikia vertingą informaciją įmonėms, norinčioms geriau suprasti savo klientų poreikius ir lūkesčius. Vienas iš efektyviausių būdų išgauti įžvalgas iš atsiliepimų yra naudoti nuotaikų analizę. Šiame tyrime aptariama, kiek klientų yra patenkinti ir nepatenkinti draudimo sektoriaus teikiamomis paslaugomis bei siūlomais produktais, taip pat nuotaikų priskyrimui buvo naudojami skirtingi vektorizavimo ir klasifikavimo metodai, kad būtų pasiekti geriausi rezultatai. Analizei atlikti naudojami du duomenų rinkiniai – produkto įsigijimo ir atsiliepimai apie žalos išmokėjimą po draudiminio įvykio. Tyrime naudojami du vektorizavimo būdai – žodžių maišo ir TF-IDF bei trys klasifikavimo metodai: atraminių vektorių, naiviojo Bajeso bei ilgalaikės trumposios atminties modelis. Atlikus tyrimą gauta, jog klientų atsiliepimų nuotaikas geriausiai klasifikuoja naiviojo Bajeso klasifikatorius su TF-IDF vektorizavimo būdu, kai tikslumas siekia 91% abiem duomenų rinkiniams. Atsiliepimams po produkto įsigijimo gautos preciziškumo ir atkūrimo metrikos teigiamam sentimentui 93% ir 97% atitinkamai, neigiamai klasei 73% ir 55%. Teigiamai klasei po žalų atlyginimo gautas preciziškumas 93% ir atkūrimo metrika 96%, o neigiamai – 82% ir 72% . Pritaikius atraminių vektorių klasifikatorių su skirtingomis vektorizavimo technikomis gauta tikslumo įvertis 89%

The analysis of eigenvalue class of methods for estimating signal parameters

Darbe atlikta tikrinių reikšmių klasės metodų taikymo signalų įvertinimui lyginamoji analizė. Išnagrinėti pagrindiniai signalo parametrai. Aprašyti dažnių įvertinimo metodai: MUSIC ir Pisarenko. Pateikti šių metodų matematiniai aprašymai ir juos realizuojančios MATLAB programos. Atlikti skaitiniai eksperimentai, kurie parodė MUSIC ir Pisarenko metodų veikimo principus, privalumus ir trukumus. Atlikti eksperimentai su realiais kalbos signalais, kurių dažniai įvertinami FFT ir MUSIC metodais. Eksperimentiškai nustatyti pagrindinių lietuvių kalbos balsių fundamentalieji dažniai.The analysis of eigenvalue class of methods for estimating signal parameters has been carried out in this work. The main signal parameters have been investigated. Two methods (MUSIC and Pisarenko) for frequency estimation have been described. Their mathematical description and MATLAB programs have been presented. Simulations with MUSIC and Pisarenko methods have been carried out. Experiments with real speech signals to estimate frequencies by the FFT and MUSIC methods have been done. The fundamental frequencies of the main Lithuanian language vowels have been estimated.Švietimo akademijaVytauto Didžiojo universiteta

Highlighting interlanguage phoneme differences based on similarity matrices and convolutional neural network

The goal of this research is to find a way of highlighting the acoustic differences between consonant phonemes of the Polish and Lithuanian languages. For this purpose, similarity matrices are employed based on speech acoustic parameters combined with a convolutional neural network (CNN). In the first experiment, we compare the effectiveness of the similarity matrices applied to discerning acoustic differences between consonant phonemes of the Polish and Lithuanian languages. The similarity matrices built on both an extensive set of parameters and a reduced set after removing high-correlated parameters are used. The results show that higher accuracy is obtained by the similarity matrices without discarding high-correlated parameters. In the second experiment, the averaged accuracies of the similarity matrices obtained are compared with the results provided by spectrograms combined with CNN, as well as the results of the vectors containing acoustic parameters and two baseline classifiers, namely k-nearest neighbors and support vector machine. The performance of the similarity matrix approach demonstrates its superiority over the methods used for compariso

Comparison of Lithuanian and Polish consonant phonemes based on acoustic analysis - preliminary results

The goal of this research is to find a set of acoustic parameters that are related to differences between Polish and Lithuanian language consonants. In order to identify these differences, an acoustic analysis is performed, and the phoneme sounds are described as the vectors of acoustic parameters. Parameters known from the speech domain as well as those from the music information retrieval area are employed. These parameters are time- and frequency-domain descriptors. English language as an auxiliary language is used in the experiments. In the first part of the experiments, an analysis of Lithuanian and Polish language samples is carried out, features are extracted, and the most discriminating ones are determined. In the second part of the experiments, automatic classification of Lithuanian/English, Polish/English, and Lithuanian/Polish phonemes is performed

An attempt to create speech synthesis model that retains Lombard effect characteristics

The speech with the Lombard effect has been extensively studied in the context of speech recognition or speech enhancement. However, few studies have investigated the Lombard effect in the context of speech synthesis. The aim of this paper is to create a mathematical model that allows for retaining the Lombard effect. These models could be used as a basis of a formant speech synthesizer. The proposed models are based on dividing the speech signal into harmonics and modeling them as the output of a SISO system whose transfer function poles are multiple, and inputs vary in time. An analysis of the Lombard effect of the synthesized signal is performed on the noise residual. The synthesized signal residual is described by vectors of acoustic parameters related to the Lombard effect. For testing the performance of the created models in various noise conditions two classifiers are employed, namely kNN and Naive Bayes. For comparison of results, we created models of sinusoids based on frequency tracks. The results show that a model based on the residual sinewave sum demonstrates the possibility of retaining the Lombard effect. Finally, future work directions are outlined in conclusions

Investigation of the Lombard effect based on a machine learning approach

The Lombard effect is an involuntary increase in the speaker’s pitch, intensity, and duration in the presence of noise. It makes it possible to communicate in noisy environments more effectively. This study aims to investigate an efficient method for detecting the Lombard effect in uttered speech. The influence of interfering noise, room type, and the gender of the person on the detection process is examined. First, acoustic parameters related to speech changes produced by the Lombard effect are extracted. Mid-term statistics are built upon the parameters and used for the self-similarity matrix construction. They constitute input data for a convolutional neural network (CNN). The self-similarity-based approach is then compared with two other methods, i.e., spectrograms used as input to the CNN and speech acoustic parameters combined with the k-nearest neighbors algorithm. The experimental investigations show the superiority of the self-similarity approach applied to Lombard effect detection over the other two methods utilized. Moreover, small standard deviation values for the self-similarity approach prove the resulting high accuracies

Evaluation of Lombard speech models in the context of speech in noise enhancement

The Lombard effect is one of the most well-known effects of noise on speech production. Speech with the Lombard effect is more easily recognizable in noisy environments than normal natural speech. Our previous investigations showed that speech synthesis models might retain Lombard-effect characteristics. In this study, we investigate several speech models, such as harmonic, source-lter, and sinusoidal, applied to Lombard speech in the context of speech enhancement. For this purpose, 100 utterances of natural speech, and 100 with the Lombard effect induced are used. The goal of this study is to check to what extent speech utterances based on these models are recognizable and at what SNR (Signal-to-Noise Ratio) level threshold a particular model stops working. For this purpose, the synthesized models and Lombard speech are mixed with babble speech and street noise recordings with different SNRs. The quality of these models is measured, employing objective indicators as well as subjective tests. Since there is no standardized measure to apply to enhanced speech, an objective measure of assessing the speech quality of a model synthesizing Lombard speech characteristics, based on a feature vector, is proposed. Our approach is then compared with the standardized metric used in telecommunications as well as with subjective test results. The experimental investigations show the superiority of the source-lter models applied to synthesize Lombard speech over other models utilized. Also, the measure proposed correlates more closely with the results of the subjective evaluation than the outcomes from the ITU-T P.563 recommendation. This was checked with a ANOVA statistical analysis

Detecting Lombard speech using deep learning approach /

Robust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks (CNNs) and various two-dimensional (2D) speech signal representations. To reduce the computational cost and not resign from the 2D representation-based approach, a strategy for threshold-based averaging of the Lombard effect detection results is introduced. The pseudocode of the averaging process is also included. A series of experiments are performed to determine the most effective network structure and the 2D speech signal representation. Investigations are carried out on German and Polish recordings containing Lombard speech. All 2D signal speech representations are tested with and without augmentation. Augmentation means using the alpha channel to store additional data: gender of the speaker, F0 frequency, and first two MFCCs. The experimental results show that Lombard and neutral speech recordings can clearly be discerned, which is done with high detection accuracy. It is also demonstrated that the proposed speech detection process is capable of working in near real-time. These are the key contributions of this work