Speech and Multimodal Interfaces Laboratory

Papers in the international journal Mathematics (Q1)

Our laboratory has published several papers in the international journal Mathematics (Scopus, Q1, WOS Q1) in a special issue of Recent Advances in Neural Networks and Applications:

  • Ivanko D, Ryumin D, Karpov A. A Review of Recent Advances on Deep Learning Methods for Audio-Visual Speech Recognition // Mathematics, 2023, vol. 11(12), 2665.

    This article provides a detailed review of recent advances in audio-visual speech recognition (AVSR) methods that have been developed over the last decade (2013–2023). Despite the recent success of audio speech recognition systems, the problem of audio-visual (AV) speech decoding remains challenging. In comparison to the previous surveys, we mainly focus on the important progress brought with the introduction of deep learning (DL) to the field and skip the description of long-known traditional “hand-crafted” methods. In addition, we also discuss the recent application of DL toward AV speech fusion and recognition. We first discuss the main AV datasets used in the literature for AVSR experiments since we consider it a data-driven machine learning (ML) task. We then consider the methodology used for visual speech recognition (VSR). Subsequently, we also consider recent AV methodology advances. We then separately discuss the evolution of the core AVSR methods, pre-processing and augmentation techniques, and modality fusion strategies. We conclude the article with a discussion on the current state of AVSR and provide our vision for future research.


  • Ryumina E, Markitantov M, Karpov A. Multi-Corpus Learning for Audio-Visual Emotions and Sentiment Recognition // Mathematics, 2023, vol. 11(16), 3519.

    Recognition of emotions and sentiment (affective states) from human audio–visual information is widely used in healthcare, education, entertainment, and other fields; therefore, it has become a highly active research area. The large variety of corpora with heterogeneous data available for the development of single-corpus approaches for recognition of affective states may lead to approaches trained on one corpus being less effective on another. In this article, we propose a multi-corpus learned audio–visual approach for emotion and sentiment recognition. It is based on the extraction of mid-level features at the segment level using two multi-corpus temporal models (a pretrained transformer with GRU layers for the audio modality and pre-trained 3D CNN with BiLSTM-Former for the video modality) and on predicting affective states using two single-corpus cross-modal gated self-attention fusion (CMGSAF) models. The proposed approach was tested on the RAMAS and CMU-MOSEI corpora. To date, our approach has outperformed state-of-the-art audio–visual approaches for emotion recognition by 18.2% (78.1% vs. 59.9%) for the CMU-MOSEI corpus in terms of the Weighted Accuracy and by 0.7% (82.8% vs. 82.1%) for the RAMAS corpus in terms of the Unweighted Average Recall.


  • Kipyatkova I, Kagirov I. Deep Models for Low-Resourced Speech Recognition: Livvi-Karelian Case // Mathematics, 2023, vol. 11(18), 3814.

    Recently, there has been a growth in the number of studies addressing the automatic processing of low-resource languages. The lack of speech and text data significantly hinders the development of speech technologies for such languages. This paper introduces an automatic speech recognition system for Livvi-Karelian. Acoustic models based on artificial neural networks with time delays and hidden Markov models were trained using a limited speech dataset of 3.5 h. To augment the data, pitch and speech rate perturbation, SpecAugment, and their combinations were employed. Language models based on 3-grams and neural networks were trained using written texts and transcripts. The achieved word error rate metric of 22.80% is comparable to other low-resource languages. To the best of our knowledge, this is the first speech recognition system for Livvi-Karelian. The results obtained can be of a certain significance for development of automatic speech recognition systems not only for Livvi-Karelian, but also for other low-resource languages, including the fields of speech recognition and machine translation systems. Future work includes experiments with Karelian data using techniques such as transfer learning and DNN language models.


  • Ryumin D, Ryumina E, Ivanko D. EMOLIPS: Towards Reliable Emotional Speech Lip-Reading // Mathematics, 2023, vol. 11(23), 4787.

    In this article, we present a novel approach for emotional speech lip-reading (EMOLIPS). This two-level approach to emotional speech to text recognition based on visual data processing is motivated by human perception and the recent developments in multimodal deep learning. The proposed approach uses visual speech data to determine the type of speech emotion. The speech data are then processed using one of the emotional lip-reading models trained from scratch. This essentially resolves the multi-emotional lip-reading issue associated with most real-life scenarios. We implemented these models as a combination of EMO-3DCNN-GRU architecture for emotion recognition and 3DCNN-BiLSTM architecture for automatic lip-reading. We evaluated the models on the CREMA-D and RAVDESS emotional speech corpora. In addition, this article provides a detailed review of recent advances in automated lip-reading and emotion recognition that have been developed over the last 5 years (2018–2023). In comparison to existing research, we mainly focus on the valuable progress brought with the introduction of deep learning to the field and skip the description of traditional approaches. The EMOLIPS approach significantly improves the state-of-the-art accuracy for phrase recognition due to considering emotional features of the pronounced audio-visual speech up to 91.9% and 90.9% for RAVDESS and CREMA-D, respectively. Moreover, we present an extensive experimental investigation that demonstrates how different emotions (happiness, anger, disgust, fear, sadness, and neutral), valence (positive, neutral, and negative) and binary (emotional and neutral) affect automatic lip-reading.