We introduce an open-source platform for annotating body-worn video (BWV) footage aimed at enhancing transparency and accountability in policing. Despite the widespread adoption of BWVs in police departments, analyzing the vast amount of footage generated has presented significant challenges. This is primarily due to resource constraints, the sensitive nature of the data, which limits widespread access, and consequently, lack of annotations for training machine learning models. Our platform, called CVAT-BWV, offers a secure, locally hosted annotation environment that integrates several AI tools to assist in annotating multimodal data. With features such as automatic speech recognition, speaker diarization, object detection, and face recognition, CVAT-BWV aims to reduce the manual annotation workload, improve annotation quality, and allow for capturing perspectives from a diverse population of annotators. This tool aims to streamline the collection of annotations and the building of models, enhancing the use of BWV data for oversight and learning purposes to uncover insights into police-civilian interactions.
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Simultaneous speech translation (SimulST) is a demanding task that involves generating translations in real-time while continuously processing speech input. This paper offers a comprehensive overview of the recent developments in SimulST research, focusing on four major challenges. Firstly, the complexities associated with processing lengthy and continuous speech streams pose significant hurdles. Secondly, satisfying real-time requirements presents inherent difficulties due to the need for immediate translation output. Thirdly, striking a balance between translation quality and latency constraints remains a critical challenge. Finally, the scarcity of annotated data adds another layer of complexity to the task. Through our exploration of these challenges and the proposed solutions, we aim to provide valuable insights into the current landscape of SimulST research and suggest promising directions for future exploration.
Social learning plays an important role in the development of human intelligence. As children, we imitate our parent's speech patterns until we are able to produce sounds; we learn from them praising us and scolding us, and as adults, we learn by working with others. In this work, we survey the degree to which this developmental paradigm -- social learning -- has been mirrored in machine learning. In particular, since learning socially requires interacting with others, we are interested in how embodied agents can and have utilised these techniques. This is especially in light of the degree to which recent advances in natural language processing (NLP) enable us to perform new forms of social learning. We look at how behaviour cloning and next-token prediction mirror human imitation, how learning from human feedback mirrors human education, and how we can go further to enable fully communicative agents that learn from each other. We find that while individual social learning techniques have been used successfully, there has been little unifying work showing how to bring them together into socially embodied agents.
Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm detection have been carried out on text only, sarcasm detection often requires additional information present in tonality, facial expression, and contextual images. This has led to the introduction of multimodal models, opening the possibility to detect sarcasm in multiple modalities such as audio, images, text, and video. In this paper, we present the first comprehensive survey on multimodal sarcasm detection - henceforth MSD - to date. We survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. We also present future research directions in MSD.
Speaker recognition technologies have been successfully applied in diverse domains, benefiting from the advance of deep learning. Nevertheless, current efforts are still subject to the lack of labeled data. Such issues have been attempted in computer vision, through semi-supervised learning (SSL) that assigns pseudo labels for unlabeled data, undertaking the role of labeled ones. Through our empirical evaluations, the state-of-the-art SSL methods show unsatisfactory performance in speaker recognition tasks, due to the imbalance between the quantity and quality of pseudo labels. Therefore, in this work, we propose a two-stage SSL framework, with the aim to address the data scarcity challenge. We first construct an initial contrastive learning network, where the encoder outputs the embedding representation of utterances. Furthermore, we construct an iterative holistic semi-supervised learning network that involves a clustering strategy to assign pseudo labels, and a gated label learning (GLL) strategy to further select reliable pseudo-label data. Systematical evaluations show that our proposed framework achieves superior performance in speaker recognition than the state-of-the-art methods, matching the performance of supervised learning.
Contextualized Speech Recognition: Rethinking Second-Pass Rescoring with Generative Large Language Models
PDF ↗Automatic Speech Recognition (ASR) systems have witnessed notable advancements in recent years. Contextualized ASR tasks require recognizing speech not as isolated utterances but within the broader context in which they occur. Conventional approaches often employ a second-pass paradigm to re-rank initial transcriptions, yet they risk propagating errors across candidate hypotheses, thereby compromising recognition precision. In this study, we introduce a novel framework that diverges from typical second-pass rescoring methods. Given n-best hypotheses, we leverage prompting with a large language model for contextualized second-pass generation. Besides pursuing higher accuracy, we aim to explore the performance boundaries without substantially altering the underlying pre-trained speech and language models. We investigate the effectiveness of the proposed paradigm through zero-shot prompting and strategic low-rank adaptation tuning. On the multi-accent spoken reading comprehension benchmark SQuAD-SRC, both prompting and fine-tuned models outperform the 1-best ASR hypothesis, achieving notable relative Word Error Rate (WER) improvements of 13.6% and 45.9%, respectively. The results suggest that the proposed approach enhances transcription accuracy and contextual understanding.
Innovative Directional Encoding in Speech Processing: Leveraging Spherical Harmonics Injection for Multi-Channel Speech Enhancement
PDF ↗Multi-channel speech enhancement leverages multiple microphones to extract target speech signals amid background noise. Effectively utilizing directional cues is key for robust enhancement. While deep learning shows promise for multi-channel speech processing, most methods operate on short-time Fourier transform (STFT) coefficients directly. We propose using spherical harmonics transform (SHT) coefficients as auxiliary inputs to models. which concisely represent spatial distributions. SHT allows signals from varying numbers of microphones to be converted into coefficients of a consistent dimension. The proposed technique enables a single model to generalize to microphone arrays with varying configurations, rather than requiring a specialized model for each array layout. We present two architectures with SHT-based auxiliary inputs: parallel and serial. Specifically, the parallel model contains two encoders - one for STFT and another for SHT. By fusing both encoders' outputs in the decoder to estimate the enhanced STFT, it effectively incorporates spatial context. For the serial approach, we first apply SHT to the signals and then take STFT of the transformed signals as network inputs. Evaluations of the TIMIT dataset under fluctuating noise and reverberation demonstrate our model outperforms established benchmarks. Remarkably, these results are attained with reduced computations and parameters. Furthermore, experiments on the MS-SNSD dataset show the proposed method can enhance the generalization ability of networks. The source code is publicly accessible at https://github.com/Pandade1997/SH_injection.
The integration of visual cues has revitalized the performance of the target speech extraction task, elevating it to the forefront of the field. Nevertheless, this multi-modal learning paradigm often encounters the challenge of modality imbalance. In audio-visual target speech extraction tasks, the audio modality tends to dominate, potentially overshadowing the importance of visual guidance. To tackle this issue, we propose AVSepChain, drawing inspiration from the speech chain concept. Our approach partitions the audio-visual target speech extraction task into two stages: speech perception and speech production. In the speech perception stage, audio serves as the dominant modality, while visual information acts as the conditional modality. Conversely, in the speech production stage, the roles are reversed. This transformation of modality status aims to alleviate the problem of modality imbalance. Additionally, we introduce a contrastive semantic matching loss to ensure that the semantic information conveyed by the generated speech aligns with the semantic information conveyed by lip movements during the speech production stage. Through extensive experiments conducted on multiple benchmark datasets for audio-visual target speech extraction, we showcase the superior performance achieved by our proposed method.
Bridge to Non-Barrier Communication: Gloss-Prompted Fine-Grained Cued Speech Gesture Generation with Diffusion Model
PDF ↗Cued Speech (CS) is an advanced visual phonetic encoding system that integrates lip reading with hand codings, enabling people with hearing impairments to communicate efficiently. CS video generation aims to produce specific lip and gesture movements of CS from audio or text inputs. The main challenge is that given limited CS data, we strive to simultaneously generate fine-grained hand and finger movements, as well as lip movements, meanwhile the two kinds of movements need to be asynchronously aligned. Existing CS generation methods are fragile and prone to poor performance due to template-based statistical models and careful hand-crafted pre-processing to fit the models. Therefore, we propose a novel Gloss-prompted Diffusion-based CS Gesture generation framework (called GlossDiff). Specifically, to integrate additional linguistic rules knowledge into the model. we first introduce a bridging instruction called Gloss, which is an automatically generated descriptive text to establish a direct and more delicate semantic connection between spoken language and CS gestures. Moreover, we first suggest rhythm is an important paralinguistic feature for CS to improve the communication efficacy. Therefore, we propose a novel Audio-driven Rhythmic Module (ARM) to learn rhythm that matches audio speech. Moreover, in this work, we design, record, and publish the first Chinese CS dataset with four CS cuers. Extensive experiments demonstrate that our method quantitatively and qualitatively outperforms current state-of-the-art (SOTA) methods. We will release the code and data at glossdiff.github.io/.
While far-field multi-talker mixtures are recorded, each speaker can wear a close-talk microphone so that close-talk mixtures can be recorded at the same time. Although each close-talk mixture has a high signal-to-noise ratio (SNR) of the wearer, it has a very limited range of applications, as it also contains significant cross-talk speech by other speakers and is not clean enough. In this context, we propose a novel task named \textit{cross-talk reduction} (CTR) which aims at reducing cross-talk speech, and a novel solution named CTRnet which is based on unsupervised or weakly-supervised neural speech separation. In unsupervised CTRnet, close-talk and far-field mixtures are stacked as input for a DNN to estimate the close-talk speech of each speaker. It is trained in an unsupervised, discriminative way such that the DNN estimate for each speaker can be linearly filtered to cancel out the speaker's cross-talk speech captured at other microphones. In weakly-supervised CTRnet, we assume the availability of each speaker's activity timestamps during training, and leverage them to improve the training of unsupervised CTRnet. Evaluation results on a simulated two-speaker CTR task and on a real-recorded conversational speech separation and recognition task show the effectiveness and potential of CTRnet.
Transformer models have been successful in various sequence processing tasks, but the self-attention mechanism's computational cost limits its practicality for long sequences. Although there are existing attention variants that improve computational efficiency, they have a limited ability to abstract global information effectively based on their hand-crafted mixing strategies. On the other hand, state-space models (SSMs) are tailored for long sequences but cannot capture complicated local information. Therefore, the combination of them as a unified token mixer is a trend in recent long-sequence models. However, the linearized attention degrades performance significantly even when equipped with SSMs. To address the issue, we propose a new method called LongVQ. LongVQ uses the vector quantization (VQ) technique to compress the global abstraction as a length-fixed codebook, enabling the linear-time computation of the attention matrix. This technique effectively maintains dynamic global and local patterns, which helps to complement the lack of long-range dependency issues. Our experiments on the Long Range Arena benchmark, autoregressive language modeling, and image and speech classification demonstrate the effectiveness of LongVQ. Our model achieves significant improvements over other sequence models, including variants of Transformers, Convolutions, and recent State Space Models.
With the development of AI-Generated Content (AIGC), text-to-audio models are gaining widespread attention. However, it is challenging for these models to generate audio aligned with human preference due to the inherent information density of natural language and limited model understanding ability. To alleviate this issue, we formulate the BATON, the first framework specifically designed to enhance the alignment between generated audio and text prompt using human preference feedback. Our BATON comprises three key stages: Firstly, we curated a dataset containing both prompts and the corresponding generated audio, which was then annotated based on human feedback. Secondly, we introduced a reward model using the constructed dataset, which can mimic human preference by assigning rewards to input text-audio pairs. Finally, we employed the reward model to fine-tune an off-the-shelf text-to-audio model. The experiment results demonstrate that our BATON can significantly improve the generation quality of the original text-to-audio models, concerning audio integrity, temporal relationship, and alignment with human preference. Project page is available at https://baton2024.github.io.
Audio self-supervised learning (SSL) pre-training, which aims to learn good representations from unlabeled audio, has made remarkable progress. However, the extensive computational demands during pre-training pose a significant barrier to the potential application and optimization of audio SSL models. In this paper, inspired by the success of data2vec 2.0 in image modality and Audio-MAE in audio modality, we introduce Efficient Audio Transformer (EAT) to further improve the effectiveness and efficiency in audio SSL. The proposed EAT adopts the bootstrap self-supervised training paradigm to the audio domain. A novel Utterance-Frame Objective (UFO) is designed to enhance the modeling capability of acoustic events. Furthermore, we reveal that the masking strategy is critical in audio SSL pre-training, and superior audio representations can be obtained with large inverse block masks. Experiment results demonstrate that EAT achieves state-of-the-art (SOTA) performance on a range of audio-related tasks, including AudioSet (AS-2M, AS-20K), ESC-50, and SPC-2, along with a significant pre-training speedup up to ~15x compared to existing audio SSL models.
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
PDF ↗Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices. However, the pursuit of accuracy in current studies leads to large, long-timestep SNNs, conflicting with the resource constraints of these devices. In order to design lightweight and efficient SNNs, we propose a new approach named LitE-SNN that incorporates both spatial and temporal compression into the automated network design process. Spatially, we present a novel Compressive Convolution block (CompConv) to expand the search space to support pruning and mixed-precision quantization. Temporally, we are the first to propose a compressive timestep search to identify the optimal number of timesteps under specific computation cost constraints. Finally, we formulate a joint optimization to simultaneously learn the architecture parameters and spatial-temporal compression strategies to achieve high performance while minimizing memory and computation costs. Experimental results on CIFAR-10, CIFAR-100, and Google Speech Command datasets demonstrate our proposed LitE-SNNs can achieve competitive or even higher accuracy with remarkably smaller model sizes and fewer computation costs.
Conditional human motion synthesis (HMS) aims to generate human motion sequences that conform to specific conditions. Text and audio represent the two predominant modalities employed as HMS control conditions. While existing research has primarily focused on single conditions, the multi-condition human motion synthesis remains underexplored. In this study, we propose a multi-condition HMS framework, termed MCM, based on a dual-branch structure composed of a main branch and a control branch. This framework effectively extends the applicability of the diffusion model, which is initially predicated solely on textual conditions, to auditory conditions. This extension encompasses both music-to-dance and co-speech HMS while preserving the intrinsic quality of motion and the capabilities for semantic association inherent in the original model. Furthermore, we propose the implementation of a Transformer-based diffusion model, designated as MWNet, as the main branch. This model adeptly apprehends the spatial intricacies and inter-joint correlations inherent in motion sequences, facilitated by the integration of multi-wise self-attention modules. Extensive experiments show that our method achieves competitive results in single-condition and multi-condition HMS tasks.
Detecting synthetic from real speech is increasingly crucial due to the risks of misinformation and identity impersonation. While various datasets for synthetic speech analysis have been developed, they often focus on specific areas, limiting their utility for comprehensive research. To fill this gap, we propose the Speech-Forensics dataset by extensively covering authentic, synthetic, and partially forged speech samples that include multiple segments synthesized by different high-quality algorithms. Moreover, we propose a TEmporal Speech LocalizaTion network, called TEST, aiming at simultaneously performing authenticity detection, multiple fake segments localization, and synthesis algorithms recognition, without any complex post-processing. TEST effectively integrates LSTM and Transformer to extract more powerful temporal speech representations and utilizes dense prediction on multi-scale pyramid features to estimate the synthetic spans. Our model achieves an average mAP of 83.55% and an EER of 5.25% at the utterance level. At the segment level, it attains an EER of 1.07% and a 92.19% F1 score. These results highlight the model's robust capability for a comprehensive analysis of synthetic speech, offering a promising avenue for future research and practical applications in this field.
The emergence of AIGC has brought attention to the issue of generating realistic deceptive content. While AIGC has the potential to revolutionize content creation, it also facilitates criminal activities. Specifically, the manipulation of speech has been exploited in tele-fraud and financial fraud schemes, posing a significant threat to societal security. Current deep learning-based methods for detecting forged speech extract mixed features from the original speech, which often contain redundant information. Moreover, these methods fail to consider the distinct characteristics of human voice-specific features and the diversity of background environmental sounds. This paper introduces a framework called Discriminative fEature dEcoupling enhanceMent (DEEM) for detecting speech forgery. Initially, the framework decouples the original speech into human voice features and background sound features. Subsequently, DEEM enhances voice-specific features through temporal dimension aggregation and improves continuity-related features in the background sound map via spectral-dimension aggregation. By employing the decoupling enhancement features, extensive experiments demonstrate that DEEM achieves an accuracy improvement of over 5% on FoR dataset compared to the state-of-the-art methods.
In this demo, we present NarrativePlay -- an innovative system enabling users to role-play a fictional character and interact with dynamically generated narrative environments. Unlike existing predefined sandbox approaches, NarrativePlay centres around the main storyline events extracted from the narrative, allowing users to experience the story from the perspective of a character they chose. To design versatile AI agents for diverse scenarios, we employ a framework built on a Large Language Models (LLMs) to extract detailed character traits from text. We also incorporate automatically generated visual displays of narrative settings, character portraits, and character speech, greatly enhancing the overall user experience.
Large language models (LLMs) have exhibited remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. Despite the recent success, current LLMs are not capable of processing complex audio information or conducting spoken conversations (like Siri or Alexa). In this work, we propose a multi-modal AI system named AudioGPT, which complements LLMs (i.e., ChatGPT) with 1) foundation models to process complex audio information and solve numerous understanding and generation tasks; and 2) the input/output interface (ASR, TTS) to support spoken dialogue. With an increasing demand to evaluate multi-modal LLMs of human intention understanding and cooperation with foundation models, we outline the principles and processes and test AudioGPT in terms of consistency, capability, and robustness. Experimental results demonstrate the capabilities of AudioGPT in solving 16 AI tasks with speech, music, sound, and talking head understanding and generation in multi-round dialogues, which empower humans to create rich and diverse audio content with unprecedented ease. Code can be found in https://github.com/AIGC-Audio/AudioGPT
A robotic workshop assistant has been a long-standing grand challenge for robotics, speech, computer vision, and artificial intelligence (AI) research. We revisit the goal of visual identification of tools from human queries in the current era of Large Vision-and-Language models (like GPT-4). We find that current off-the-shelf models (that are trained on internet images) are unable to overcome the domain shift and unable to identify small, obscure tools in cluttered environments. Furthermore, these models are unable to match tools to their intended purpose or affordances. We present a novel system for online domain adaptation that can be run directly on a small on-board processor. The system uses Hyperdimensional Computing (HD), a fast and efficient neuromorphic method. We adapted CLIP to work with explicit ("I need the hammer") and implicit purpose-driven queries ("Drive these nails"), and even with depth images as input. This demo allows the user to try out various real tools and interact via free-form audio.