Audio-Visual Tibetan Speech Recognition Based on a Deep Dynamic Bayesian Network for Natural Human Robot Interaction

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.

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.

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

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

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

This work extends and improves a recently introduced (Dec. 2007) dynamic Bayesian network (DBN) based audio-visual automatic speech recognition (AV-ASR) system. That system models the audio and visual components of speech as being composed of the same sub-word units when, in fact, this is not psycholinguistically true. We extend the system to model the audio and visual streams as being composed of separate, yet related, sub-word units. We also introduce a novel stream weighting structure incorporated into the model itself. In doing so, our system makes improvements in word error rate (WER) and overall recognition accuracy in a large vocabulary continuous speech recognition task (LVCSR). The ldquobestrdquo performing proposed system attains a WER of 66.71%whereas the ldquobestrdquo baseline system performs at a WER of 64.30%. The proposed system also improves accuracy to 45.95% from 39.40%.

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.

Sensory information is indispensable for living things. It is also important for living things to integrate multiple types of senses to understand their surroundings. In human communications, human beings must further integrate the multimodal senses of audition and vision to understand intention. In this paper, we describe speech related modalities since speech is the most important media to transmit human intention. To date, there have been a lot of studies concerning technologies in speech communications, but performance levels still have room for improvement. For instance, although speech recognition has achieved remarkable progress, the speech recognition performance still seriously degrades in acoustically adverse environments. On the other hand, perceptual research has proved the existence of the complementary integration of audio speech and visual face movements in human perception mechanisms. Such research has stimulated attempts to apply visual face information to speech recognition and synthesis. This paper introduces works on audio-visual speech recognition, speech to lip movement mapping for audio-visual speech synthesis, and audio-visual speech translation.

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.

Abstract. This paper presents an intelligent system for automatic bidirectional translation between sign language and spoken language, aimed at facilitating inclusive communication between deaf or hard-of-hearing individuals and hearing persons. The proposed system integrates four core modules: sign language recognition, audiovisual speech recognition, sign language synthesis, and speech synthesis. Developed with a constrained vocabulary of 84 phrases relevant to medical consultations, the system enables translation in both directions—sign-to-speech and speech-to-sign. The architecture leverages state-of-the-art deep learning techniques, including transformer-based models, neural vocoders, and avatar-driven gesture synthesis. Experimental evaluations demonstrate high accuracy in gesture and speech recognition, with strong subjective ratings for the naturalness and intelligibility of synthesized outputs. This work contributes to the advancement of accessible communication technologies and lays the groundwork for future expansion into broader domains and unconstrained dialog scenarios.

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.

Introduction to dual sensory loss issue.

Audio-visual speech recognition (AVSR) has shown impressive improvements over audio-only speech recognition in the presence of acoustic noise. However, the problems of region-of-interest detection and feature extraction may influence the recognition performance due to the visual speech information obtained typically from planar video data. In this paper, we deviate from the traditional visual speech information and propose an AVSR system integrating 3D lip information. The Microsoft Kinect multi-sensory device was adopted for data collection. The different feature extraction and selection algorithms were applied to planar images and 3D lip information, so as to fuse the planar images and 3D lip feature into the visual-3D lip joint feature. For automatic speech recognition (ASR), the fusion methods were investigated and the audio-visual speech information was integrated into a state-synchronous two stream Hidden Markov Model. The experimental results demonstrated that our AVSR system integrating 3D lip information improved the recognition performance of traditional ASR and AVSR system in acoustic noise environments.

Multimodal feature extraction and fusion for audio-visual speech recognition