Emphasizing Domain Differences Through Interactive-Augmented Prompts in Continual Audio-Visual Speech Recognition.

Audio-visual speech recognition (AVSR) is one of the most promising solutions for reliable speech recognition, particularly when audio is corrupted by noise. Additional visual information can be used for both automatic lip-reading and gesture recognition. Hand gestures are a form of non-verbal communication and can be used as a very important part of modern human–computer interaction systems. Currently, audio and video modalities are easily accessible by sensors of mobile devices. However, there is no out-of-the-box solution for automatic audio-visual speech and gesture recognition. This study introduces two deep neural network-based model architectures: one for AVSR and one for gesture recognition. The main novelty regarding audio-visual speech recognition lies in fine-tuning strategies for both visual and acoustic features and in the proposed end-to-end model, which considers three modality fusion approaches: prediction-level, feature-level, and model-level. The main novelty in gesture recognition lies in a unique set of spatio-temporal features, including those that consider lip articulation information. As there are no available datasets for the combined task, we evaluated our methods on two different large-scale corpora—LRW and AUTSL—and outperformed existing methods on both audio-visual speech recognition and gesture recognition tasks. We achieved AVSR accuracy for the LRW dataset equal to 98.76% and gesture recognition rate for the AUTSL dataset equal to 98.56%. The results obtained demonstrate not only the high performance of the proposed methodology, but also the fundamental possibility of recognizing audio-visual speech and gestures by sensors of mobile devices.

The impact of automatic exaggeration of the visual articulatory features of a talker on the intelligibility of spectrally distorted speech

HMM-based audio-visual speech recognition (AVSR) systems have shown success in continuous speech recognition by combining visual and audio information, especially in noisy environments. In this paper we study how to improve decision trees used to create context classes in HMM-based AVSR systems. Traditionally, visual models have been trained with the same context classes as the audio only models. In this paper we investigate the use of separate decision trees to model the context classes for the audio and visual streams independently. Additionally we investigate the use of viseme classes in the decision tree building for the visual stream. On experiments with a 37-speaker 1.5 hours test set (about 12000 words) of continuous digits in noise, we obtain about a 3% absolute (20% relative) gain on AVSR performance by using separate decision trees for the audio and visual streams when using viseme classes in decision tree building for the visual stream.

Audio-visual speech recognition is a natural and robust approach to improving human-robot interaction in noisy environments. Although multi-stream Dynamic Bayesian Network and coupled HMM are widely used for audio-visual speech recognition, they fail to learn the shared features between modalities and ignore the dependency of features among the frames within each discrete state. In this paper, we propose a Deep Dynamic Bayesian Network (DDBN) to perform unsupervised extraction of spatial-temporal multimodal features from Tibetan audio-visual speech data and build an accurate audio-visual speech recognition model under a no frame-independency assumption. The experiment results on Tibetan speech data from some real-world environments showed the proposed DDBN outperforms the state-of-art methods in word recognition accuracy.

Multimodal feature extraction and fusion for audio-visual speech recognition

This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal modality, which is commonly used in IoT and embedded systems. Examples of suitable IoT applications include in-cabin speech recognition systems for driving systems, AVSR in augmented reality environments, and interactive applications such as virtual aquariums. The application of multimodal sensor data for IoT applications requires efficient information processing, to meet the hardware constraints of IoT devices. The proposed multimodal AVSR GAN architecture is composed of a discriminator and a generator, each of which is a two-stream network, corresponding to the audio stream information and the visual stream information, respectively. To validate this approach, we used augmented data from well-known datasets (LRS2-Lip Reading Sentences 2 and LRS3) in the training process, and testing was performed using the original data. The research and experimental results showed that the proposed multimodal AVSR GAN architecture improved the AVSR classification accuracy. Furthermore, in this study, we discuss the domain of GANs and provide a concise summary of the proposed GANs.

There has been a long term interest in using visual information to improve automatic speech recognition (ASR) system performance. Both audio and visual information are required in conventional audio visual speech recognition (AVSR) systems. This limits their wider applications when visual modality is not present. To this end, one possible solution is to use acoustic-to-visual (A2V) inversion techniques to generate visual features. Previous research in this direction used synthetic acoustic-articulatory parallel data in inversion model training. The acoustic mismatch between the audio-visual (AV) parallel data and target data was not considered. In addition, the target language to apply these technologies has been focused on English. In this article, a real 3D Audio-Visual Mandarin Continuous Speech (3DAV-MCS) corpus was used to train deep neural network based A2V inversion models. Cross-domain adaptation of the inversion models allows suitable visual features to be generated from acoustic data of mismatched domains. The proposed cross-domain deep visual feature generation techniques were evaluated on two state-of-the-art Mandarin speech recognition tasks: DAPRA GALE broadcast transcription and BOLT conversational telephone speech recognition. The AVSR systems constructed using the cross-domain generated visual features consistently outperformed the baseline convolutional neural network (CNN) ASR systems by up to 3.3% absolute (9.1% relative) character error rate (CER) reductions after both speaker adaptive training and sequence discriminative training were performed.

Since attention mechanism was introduced in neural machine translation, attention has been combined with the long short-term memory (LSTM) or replaced the LSTM in a transformer model to overcome the sequence-to-sequence (seq2seq) problems with the LSTM. In contrast to the neural machine translation, audio–visual speech recognition (AVSR) may provide improved performance by learning the correlation between audio and visual modalities. As a result that the audio has richer information than the video related to lips, AVSR is hard to train attentions with balanced modalities. In order to increase the role of visual modality to a level of audio modality by fully exploiting input information in learning attentions, we propose a dual cross-modality (DCM) attention scheme that utilizes both an audio context vector using video query and a video context vector using audio query. Furthermore, we introduce a connectionist-temporal-classification (CTC) loss in combination with our attention-based model to force monotonic alignments required in AVSR. Recognition experiments on LRS2-BBC and LRS3-TED datasets showed that the proposed model with the DCM attention scheme and the hybrid CTC/attention architecture achieved at least a relative improvement of 7.3% on average in the word error rate (WER) compared to competing methods based on the transformer model.

Human computer interaction (HCI) is very crucial in our day-to-day activity. Speech is one of the essential and intuitive ways to interact with machines such as Smartphone, which has multiple sensors as microphone, camera, etc. An efficient performance speech recognition system improves interaction between man and machines by making latter more receptive to user needs. Such system has Automatic speech recognition (ASR) engine, which is facing a unique challenge of accuracy in recognition rate. By integrating acoustic signal feature vectors with the visual features, a more robust audiovisual speech recognition engine (AVSR) could be developed for real environmental scenarios. This paper presents past research and development in the field of ASR and AVSR technologies. It describes key technological perspective and admiration of the fundamental progress in ASR and AVSR. The objective of this review is to summarize and compare some of the well-known methods experimented by previous researchers, and to conclude with direction on future research proficiency in HCI system using ASR and AVSR engine.

Audio–visual speech recognition based on regulated transformer and spatio–temporal fusion strategy for driver assistive systems

Audio-visual speech recognition based on joint training with audio-visual speech enhancement for robust speech recognition

The model of automatic speech recognition (ASR) has an overfitting problem in the task of low-resource speech recognition, due to the lack of training data. Different from conventional methodologies that rely on signal acoustic modality, we proposed to design a novel ASR model for the low-resource speech recognition task by fusing acoustic and visual modalities, standing from the perspective of the physiological mechanism of human pronunciation. In this paper, the progressive down-sampling (PD) and grouped self-attention mechanism (GASM) are applied to design the audio-visual speech recognition (AVSR) framework, called PD-GASM, for the low-resource speech recognition task. In particular, The PD algorithm reduces computational resource consumption during the training process. The GASM algorithm is used to help the trained model handle both short and long sequences; therefore, the proposed model can be more suitable for processing spoken dialogue sequences. Our proposed method has been evaluated based on the Tujia language audio-visual data corpus, miniLRW and miniLRS2 audio-visual data corpus. The experimental results show that the best character error rates can achieve 35.15%, 14.89%, and 16.33 %, respectively. Our research has significant implications for expanding the application of AVSR in the field of low-resource speech recognition.

Speech recognition in Human-Robot Interaction (HRI) fully relies on audio-based Automatic Speech Recognition. However, speech recognition that relies solely on audio faces significant challenges in noisy environments and may lead to poor performance in such environments. One approach to address this is to also use lipreading in combination with traditional speech recognition. Recent work has shown that audiovisual speech recognition (AVSR) can achieve a Word Error Rate (WER) of only 0.9% on the dataset LRS3. In this paper, we assess the potential of combining audio with lipreading on a social robot platform, Pepper, which has not yet been widely tested for AVSR. Given that prior research has focused on non-robotic domains, it remains unclear whether such models can generalize well to social robot environments. We systematically evaluate and compare the performance of established offline and real-time audiovisual models with their audio-only counterparts. The experiments were conducted in both a controlled laboratory setting and a dynamic and noisy public environment. We evaluated the data using WER and also measured the inference latency of real-time models via Real-Time Factor and Words Per Second rates. The results demonstrate real-time performance for audio-only speech recognition across all latency metrics and near real-time performance for models that combine audio with lipreading. We also explored factors that might influence the inference performance of these models to understand how much video contributes to the audio. This includes factors related to (1) environmental and temporal variations, (2) model behavior, and (3) implementation choices. Our findings indicate that for now the audio-only models outperform the audiovisual models on a social robot platform, in contrast to what has been reported in the benchmarked literature. We conclude that more work is still needed to benefit from lipreading in HRI.

Audio-visual speech recognition (AVSR) leverages complementary visual cues to improve speech recognition. However, in real-world scenarios, both modalities may suffer from noise or occlusion. In such scenarios, most existing fusion strategies overlook the variation in modality-specific quality under different degradation conditions. This limitation may lead to dominance of corrupted modality in the fusion process, resulting in worse AVSR performance than unimodal systems, termed as Corrupted Modality Bias (CMB) in this work. To address this, a self-supervised speech representation learning framework, called AV-RISE, is proposed to employ teacher-student self-distillation to robustly reconstruct clean speech representations from corrupted audio-visual inputs. A hierarchical fusion mechanism is designed to progressively refine audio and visual representations by integrating the Suppression and Enhancement Interaction (SEI) module into each layer of the pre-trained encoder. In the SEI module, cross-modal suppression and modality-oriented enhancement are performed to mitigate noise-induced feature inconsistencies, which strengthens the modeling of complementary semantic representations. Extensive experiments on the LRS2 and LRS3 datasets demonstrate that AV-RISE outperforms SOTA AVSR models, especially under extreme degradation. Most importantly, the hierarchical SEI-based fusion effectively enhances reliable semantic representations to mitigate CMB, by evaluating feature similarities between clean and noise samples.

We describe an audio-visual automatic continuous speech recognition system, which significantly improves speech recognition performance over a wide range of acoustic noise levels, as well as under clean audio conditions. The system utilizes facial animation parameters (FAPs) supported by the MPEG-4 standard for the visual representation of speech. We also describe a robust and automatic algorithm we have developed to extract FAPs from visual data, which does not require hand labeling or extensive training procedures. The principal component analysis (PCA) was performed on the FAPs in order to decrease the dimensionality of the visual feature vectors, and the derived projection weights were used as visual features in the audio-visual automatic speech recognition (ASR) experiments. Both single-stream and multistream hidden Markov models (HMMs) were used to model the ASR system, integrate audio and visual information, and perform a relatively large vocabulary (approximately 1000 words) speech recognition experiments. The experiments performed use clean audio data and audio data corrupted by stationary white Gaussian noise at various SNRs. The proposed system reduces the word error rate (WER) by 20% to 23% relatively to audio-only speech recognition WERs, at various SNRs (0–30 dB) with additive white Gaussian noise, and by 19% relatively to audio-only speech recognition WER under clean audio conditions.