Search Results for author: Alexey Karpov

Found 11 papers, 3 papers with code

RUSAVIC Corpus: Russian Audio-Visual Speech in Cars

no code implementations LREC 2022 Denis Ivanko, Alexandr Axyonov, Dmitry Ryumin, Alexey Kashevnik, Alexey Karpov

We present a new audio-visual speech corpus (RUSAVIC) recorded in a car environment and designed for noise-robust speech recognition.

Audio-Visual Speech Recognition Lip Reading +3

Is Everything Fine, Grandma? Acoustic and Linguistic Modeling for Robust Elderly Speech Emotion Recognition

1 code implementation7 Sep 2020 Gizem Soğancıoğlu, Oxana Verkholyak, Heysem Kaya, Dmitrii Fedotov, Tobias Cadèe, Albert Ali Salah, Alexey Karpov

Acoustic and linguistic analysis for elderly emotion recognition is an under-studied and challenging research direction, but essential for the creation of digital assistants for the elderly, as well as unobtrusive telemonitoring of elderly in their residences for mental healthcare purposes.

Speech Emotion Recognition

Class-based LSTM Russian Language Model with Linguistic Information

no code implementations LREC 2020 Irina Kipyatkova, Alexey Karpov

We achieved WER of 14. 94 {\%} at our own speech corpus of continuous Russian speech that is 15 {\%} relative reduction with respect to the baseline 3-gram model.

Language Modelling speech-recognition +1

TheRuSLan: Database of Russian Sign Language

no code implementations LREC 2020 Ildar Kagirov, Denis Ivanko, Dmitry Ryumin, Alex Axyonov, er, Alexey Karpov

The database includes lexical units (single words and phrases) from Russian sign language within one subject area, namely, {``}food products at the supermarket{''}, and was collected using MS Kinect 2. 0 device including both FullHD video and the depth map modes, which provides new opportunities for the lexicographical description of the Russian sign language vocabulary and enhances research in the field of automatic gesture recognition.

Gesture Recognition Sign Language Recognition

Cross-Corpus Data Augmentation for Acoustic Addressee Detection

no code implementations WS 2019 Oleg Akhtiamov, Ingo Siegert, Alexey Karpov, Wolfgang Minker

Mixup is shown to be beneficial for merging acoustic data (extracted features but not raw waveforms) from different domains that allows us to reach a higher classification performance on human-machine AD and also for training a multipurpose neural network that is capable of solving both human-machine and adult-child AD problems.

Cross-corpus Data Augmentation +1

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