In this paper we explore audiovisual emotion recognition under noisy acoustic conditions with a focus on speech features. We attempt to answer the following research questions: (i) How does speech emotion recognition perform on noisy data? and (ii) To what extend does a multimodal approach improve the accuracy and compensate for potential performance degradation at different noise levels? We present an analytical investigation on two emotion datasets with superimposed noise at different signal-to-noise ratios, comparing three types of acoustic features. Visual features are incorporated with a hybrid fusion approach: The first neural network layers are separate modality-specific ones, followed by at least one shared layer before the final prediction. The results show a significant performance decrease when a model trained on clean audio is applied to noisy data and that the addition of visual features alleviates this effect.
Tampere University（坦佩雷大学）；Aarhus University（奥胡斯大学）
In this paper, we consider the problem of multimodal data analysis with a use case of audiovisual emotion recognition. We propose an architecture capable of learning from raw data and describe three variants of it with distinct modality fusion mechanisms. While most of the previous works consider the ideal scenario of presence of both modalities at all times during inference, we evaluate the robustness of the model in the unconstrained settings where one modality is absent or noisy, and propose a method to mitigate these limitations in a form of modality dropout. Most importantly, we find that…
University of Coimbra（科英布拉大学）；Centre for Informatics and Systems of the University of Coimbra（科英布拉大学信息学与系统中心）
We present ESSYS* Sharing #UC, an audiovisual installation artwork that reflects upon the emotional context related to the university and the city of Coimbra, based on the data shared about them on Twitter. The installation was presented in an urban art gallery of Círculo de Artes Plásticas de Coimbra during the summer and autumn of 2021. In the installation space, one may see a collection of typographic posters displaying the tweets and listening to an ever-changing ambient sound. The present audiovisuals are created by an autonomous computational creative approach, which employs a neural cla…
National Institute of Informatics（国立信息学研究所）；The University of Edinburgh（爱丁堡大学）
An audiovisual speaker conversion method is presented for simultaneously transforming the facial expressions and voice of a source speaker into those of a target speaker. Transforming the facial and acoustic features together makes it possible for the converted voice and facial expressions to be highly correlated and for the generated target speaker to appear and sound natural. It uses three neural networks: a conversion network that fuses and transforms the facial and acoustic features, a waveform generation network that produces the waveform from both the converted facial and acoustic featur…
AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues
UC Berkeley（加州大学伯克利分校）；South China University of Technology（华南理工大学）；Zhejiang University（浙江大学）；National Taiwan University（台湾大学）；University of Southern California（美国南加州大学）
University of Augsburg（奥格斯堡大学）；University of Nottingham（诺丁汉大学）
In this paper, we present our submission to 3rd Affective Behavior Analysis in-the-wild (ABAW) challenge. Learningcomplex interactions among multimodal sequences is critical to recognise dimensional affect from in-the-wild audiovisual data. Recurrence and attention are the two widely used sequence modelling mechanisms in the literature. To clearly understand the performance differences between recurrent and attention models in audiovisual affect recognition, we present a comprehensive evaluation of fusion models based on LSTM-RNNs, self-attention and cross-modal attention, trained for valence …
Beyond the Mouth: Upper-Face Affective Cues in Audiovisual Sentence Recognition under Acoustic Uncertainty
Faculty of Education and Psychology, University of Oulu, Finland（奥卢大学教育与心理学学院，芬兰）；Center for Machine Vision and Signal Analysis, University of Oulu, Finland（奥卢大学机器视觉与信号分析中心，芬兰）
Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS)（弗劳恩霍夫智能分析与信息系统研究所）；Haus der Geschichte der Bundesrepublik Deutschland Foundation (HdG)（德意志联邦共和国历史之家基金会）；University of Applied Sciences Bonn-Rhein-Sieg（波恩-莱茵-锡格应用技术大学）；Autonomous Intelligent Systems (AIS), Computer Science Institute VI, Univ. of Bonn（波恩大学计算机科学第六研究所自主智能系统）
For research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make these different facets searchable. In particular, for oral history archives, such indexing technologies can be of great interest. These technologies can help understand the role of emotions in historical remembering. However, humans often perceive sentiments and emotions ambiguously and subjectively. Moreover, oral history interviews have multi-layered levels of complex, sometimes contradictory, sometimes ver…
Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；National Taiwan University（台湾大学）；Institute of Linguistics, Academia Sinica（中央研究院语言学研究所）
Speech perception is key to verbal communication. For people with hearing loss, the capability to recognize speech is restricted, particularly in a noisy environment or the situations without visual cues, such as lip-reading unavailable via phone call. This study aimed to understand the improvement of vocoded speech intelligibility in cochlear implant (CI) simulation through two potential methods: Speech Enhancement (SE) and Audiovisual Integration. A fully convolutional neural network (FCN) using an intelligibility-oriented objective function was recently proposed and proven to effectively fa…
Relational graph-driven differential denoising and diffusion attention fusion for multimodal conversation emotion recognition
College of Computer and Mathematics, Central South University of Forestry and Technology（中南林业科技大学计算机与数学学院）；Department of Computer Science, State University of New York, New Paltz（纽约州立大学新帕尔茨分校计算机科学系）
Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；LORIA（洛林计算机科学及其应用研究所）
Deep latent variable generative models based on variational autoencoder (VAE) have shown promising performance for audiovisual speech enhancement (AVSE). The underlying idea is to learn a VAEbased audiovisual prior distribution for clean speech data, and then combine it with a statistical noise model to recover a speech signal from a noisy audio recording and video (lip images) of the target speaker. Existing generative models developed for AVSE do not take into account the sequential nature of speech data, which prevents them from fully incorporating the power of visual data. In this paper, w…
Zhejiang Lab（之江实验室）；Dalhousie University（达尔豪斯大学）；University of Electronic Science and Technology of China（电子科技大学）；Zhejiang University（浙江大学）
Active speaker detection plays a vital role in human-machine interaction. Recently, a few end-to-end audiovisual frameworks emerged. However, these models' inference time was not explored and are not applicable for real-time applications due to their complexity and large input size. In addition, they explored a similar feature extraction strategy that employs the ConvNet on audio and visual inputs. This work presents a novel two-stream end-to-end framework fusing features extracted from images via VGG-M with raw Mel Frequency Cepstrum Coefficients features extracted from the audio waveform. Th…
Audiovisual speech synthesis is the problem of synthesizing a talking face while maximizing the coherency of the acoustic and visual speech. In this paper, we propose and compare two audiovisual speech synthesis systems for 3D face models. The first system is the AVTacotron2, which is an end-to-end text-to-audiovisual speech synthesizer based on the Tacotron2 architecture. AVTacotron2 converts a sequence of phonemes representing the sentence to synthesize into a sequence of acoustic features and the corresponding controllers of a face model. The output acoustic features are used to condition a…
Audio-visual information fusion enables a performance improvement in speech recognition performed in complex acoustic scenarios, e.g., noisy environments. It is required to explore an effective audio-visual fusion strategy for audiovisual alignment and modality reliability. Different from the previous end-to-end approaches where the audio-visual fusion is performed after encoding each modality, in this paper we propose to integrate an attentive fusion block into the encoding process. It is shown that the proposed audio-visual fusion method in the encoder module can enrich audio-visual represen…
Shanghai University（上海大学）；University of Leicester（莱斯特大学）
Multimodal sentiment analysis has a wide range of applications due to its information complementarity in multimodal interactions. Previous works focus more on investigating efficient joint representations, but they rarely consider the insufficient unimodal features extraction and data redundancy of multimodal fusion. In this paper, a Video-based Cross-modal Auxiliary Network (VCAN) is proposed, which is comprised of an audio features map module and a cross-modal selection module. The first module is designed to substantially increase feature diversity in audio feature extraction, aiming to imp…
Audio-Video Emotion Recognition is now attacked with Deep Neural Network modeling tools. In published papers, as a rule, the authors show only cases of the superiority in multi-modality over audio-only or video-only modality. However, there are cases superiority in uni-modality can be found. In our research, we hypothesize that for fuzzy categories of emotional events, the within-modal and inter-modal noisy information represented indirectly in the parameters of the modeling neural network impedes better performance in the existing late fusion and end-to-end multi-modal network training strate…
With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection. Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection. However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective. The present paper aims to tackle these challenges by proposing a framework that (1) uses concept-mapped lexi…
Speech is one of the most effective ways of communication among humans. Even though audio is the most common way of transmitting speech, very important information can be found in other modalities, such as vision. Vision is particularly useful when the acoustic signal is corrupted. Multi-modal speech recognition however has not yet found wide-spread use, mostly because the temporal alignment and fusion of the different information sources is challenging. This paper presents an end-to-end audiovisual speech recognizer (AVSR), based on recurrent neural networks (RNN) with a connectionist tempora…
Khalifa University（哈利法大学）；Mohammed VI Polytechnic University（穆罕默德六世理工大学）；University of Sharjah（沙迦大学）
Speech signals are subjected to more acoustic interference and emotional factors than other signals. Noisy emotion-riddled speech data is a challenge for real-time speech processing applications. It is essential to find an effective way to segregate the dominant signal from other external influences. An ideal system should have the capacity to accurately recognize required auditory events from a complex scene taken in an unfavorable situation. This paper proposes a novel approach to speaker identification in unfavorable conditions such as emotion and interference using a pre-trained Deep Neura…
University of Sharjah（沙迦大学）；The University of Tokyo（东京大学）
This work aims at intensifying text-independent speaker identification performance in real application situations such as noisy and emotional talking conditions. This is achieved by incorporating two different modules: a Computational Auditory Scene Analysis CASA based pre-processing module for noise reduction and cascaded Gaussian Mixture Model Convolutional Neural Network GMM-CNN classifier for speaker identification followed by emotion recognition. This research proposes and evaluates a novel algorithm to improve the accuracy of speaker identification in emotional and highly-noise susceptib…
We present Mirable's submission to the 2021 Emotions and Themes in Music challenge. In this work, we intend to address the question: can we leverage semi-supervised learning techniques on music emotion recognition? With that, we experiment with noisy student training, which has improved model performance in the image classification domain. As the noisy student method requires a strong teacher model, we further delve into the factors including (i) input training length and (ii) complementary music representations to further boost the performance of the teacher model. For (i), we find that model…
A Comprehensive Study on the Effectiveness of ASR Representations for Noise-Robust Speech Emotion Recognition
Graduate School of Informatics, Nagoya University（国立 Nagoya 大学信息科学研究生院）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Information Technology Center, Nagoya University（国立 Nagoya 大学信息技术中心）
Multi-Level Knowledge Distillation for Speech Emotion Recognition in Noisy Conditions
Qingdao University of Science and Technology（青岛科技大学）；National Institute of Information and Communications Technology（国立信息通信研究机构）；Tianjin University（天津大学）
Speakers usually adjust their way of talking in noisy environments involuntarily for effective communication. This adaptation is known as the Lombard effect. Although speech accompanying the Lombard effect can improve the intelligibility of a speaker's voice, the changes in acoustic features (e.g. fundamental frequency, speech intensity, and spectral tilt) caused by the Lombard effect may also affect the listener's judgment of emotional content. To the best of our knowledge, there is no published study on the influence of the Lombard effect in emotional speech. Therefore, we recorded parallel …
Despite the widespread utilization of deep neural networks (DNNs) for speech emotion recognition (SER), they are severely restricted due to the paucity of labeled data for training. Recently, segment-based approaches for SER have been evolving, which train backbone networks on shorter segments instead of whole utterances, and thus naturally augments training examples without additional resources. However, one core challenge remains for segment-based approaches: most emotional corpora do not provide ground-truth labels at the segment level. To supervisely train a segment-based emotion model on …
Data augmentation is a widely used strategy for training robust machine learning models. It partially alleviates the problem of limited data for tasks like speech emotion recognition (SER), where collecting data is expensive and challenging. This study proposes CopyPaste, a perceptually motivated novel augmentation procedure for SER. Assuming that the presence of emotions other than neutral dictates a speaker's overall perceived emotion in a recording, concatenation of an emotional (emotion E) and a neutral utterance can still be labeled with emotion E. We hypothesize that SER performance can …
Emotion labels in emotion recognition corpora are highly noisy and ambiguous, due to the annotators' subjective perception of emotions. Such ambiguity may introduce errors in automatic classification and affect the overall performance. We therefore propose a dynamic label correction and sample contribution weight estimation model. Our model is based on a standard BLSTM model with attention with two extra parameters. The first learns a new corrected label distribution, and is aimed to fix the inaccurate labels from the dataset. The other instead estimates the contribution of each sample to the …
University of Helsinki（赫尔辛基大学）；Omilia - Conversational Intelligence（Omilia对话智能公司）；Brno University of Technology（布尔诺理工大学）
When recognizing emotions from speech, we encounter two common problems: how to optimally capture emotion-relevant information from the speech signal and how to best quantify or categorize the noisy subjective emotion labels. Self-supervised pre-trained representations can robustly capture information from speech enabling state-of-the-art results in many downstream tasks including emotion recognition. However, better ways of aggregating the information across time need to be considered as the relevant emotion information is likely to appear piecewise and not uniformly across the signal. For th…
Current computational-emotion research has focused on applying acoustic properties to analyze how emotions are perceived mathematically or used in natural language processing machine learning models. While recent interest has focused on analyzing emotions from the spoken voice, little experimentation has been performed to discover how emotions are recognized in the singing voice -- both in noiseless and noisy data (i.e., data that is either inaccurate, difficult to interpret, has corrupted/distorted/nonsense information like actual noise sounds in this case, or has a low ratio of usable/unusab…
University of Hamburg（汉堡大学）；Department of Informatics, Knowledge Technology Institute（信息学系、知识技术研究所）
Speech emotion recognition (SER) is an important aspect of effective human-robot collaboration and received a lot of attention from the research community. For example, many neural network-based architectures were proposed recently and pushed the performance to a new level. However, the applicability of such neural SER models trained only on in-domain data to noisy conditions is currently under-researched. In this work, we evaluate the robustness of state-of-the-art neural acoustic emotion recognition models in human-robot interaction scenarios. We hypothesize that a robot's ego noise, room co…
EmoAttack: Utilizing Emotional Voice Conversion for Speech Backdoor Attacks on Deep Speech Classification Models
School of Cyberspace Science Xiangtan University Xiangtan, China；School of Cyberspace Science Xiangtan University Beijing, China；School of Software \& Microelectronics Peking University Beijing, China
University of Southern Queensland（南昆士兰大学）；Data61, CSIRO（联邦科学与工业研究组织数据61研究所）；Queensland University of Technology（昆士兰科技大学）；Imperial College London（帝国理工学院）；University of Augsburg（奥格斯堡大学）
Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems lack generalisation across different conditions. A key underlying reason for poor generalisation is the scarcity of emotion datasets, which is a significant roadblock to designing robust machine learning (ML) models. Recent works in SER focus on utilising multitask learning (MTL) methods to improve generalisation by learning shared representations. However, most of these studies propose MTL solutions with the requirement of meta labels for auxiliary tasks, which limits the training of SER systems. This pa…
Investigating Polyglot Speech Foundation Models for Learning Collective Emotion from Crowds
IIIT-Delhi（印度德里印度理工学院）；UPES（乌普萨拉理工学院）；V.B.S.P.U（V.B.S.P.U大学）；VIT（瓦拉纳西理工学院）；Independent Researcher（独立研究者）；L.B.R College of Engineering（L.B.R工程学院）；Reliance AI
Semi-Supervised Self-Learning Enhanced Music Emotion Recognition
School of Computer Science and Technology, Fudan University（复旦大学计算机科学与技术学院）；Ping An Technology Co., Ltd.（平安科技有限公司）；Department of Music AI and Information Technology, Central Conservatory of Music（中央音乐学院音乐人工智能与信息技术系）；Shanghai Key Laboratory of Intelligent Information Processing, Fudan University（复旦大学上海智能信息处理重点实验室）
Deep convolutional neural networks are being actively investigated in a wide range of speech and audio processing applications including speech recognition, audio event detection and computational paralinguistics, owing to their ability to reduce factors of variations, for learning from speech. However, studies have suggested to favor a certain type of convolutional operations when building a deep convolutional neural network for speech applications although there has been promising results using different types of convolutional operations. In this work, we study four types of convolutional op…
