pwc-archive/papers-with-abstracts huggingface dataset:
ReelWave: Multi-Agentic Movie Sound Generation through Multimodal LLM Conversation
Authors: Zixuan Wang, Chi-Keung Tang, Yu-Wing Tai
Date: 2025-03-10 00:00:00
Tasks: Audio Generation

Current audio generation conditioned by text or video focuses on aligning audio with text/video modalities. Despite excellent alignment results, these multimodal frameworks still cannot be directly applied to compelling movie storytelling involving multiple scenes, where "on-screen" sounds require temporally-aligned audio generation, while "off-screen" sounds contribute to appropriate environment sounds accompanied by background music when applicable. Inspired by professional movie production, this paper proposes a multi-agentic framework for audio generation supervised by an autonomous Sound Director agent, engaging multi-turn conversations with other agents for on-screen and off-screen sound generation through multimodal LLM. To address on-screen sound generation, after detecting any talking humans in videos, we capture semantically and temporally synchronized sound by training a prediction model that forecasts interpretable, time-varying audio control signals: loudness, pitch, and timbre, which are used by a Foley Artist agent to condition a cross-attention module in the sound generation. The Foley Artist works cooperatively with the Composer and Voice Actor agents, and together they autonomously generate off-screen sound to complement the overall production. Each agent takes on specific roles similar to those of a movie production team. To temporally ground audio language models, in ReelWave, text/video conditions are decomposed into atomic, specific sound generation instructions synchronized with visuals when applicable. Consequently, our framework can generate rich and relevant audio content conditioned on video clips extracted from movies.
Synchronized Video-to-Audio Generation via Mel Quantization-Continuum Decomposition
Authors: Juncheng Wang, Chao Xu, Cheng Yu, Lei Shang, Zhe Hu, Shujun Wang, Liefeng Bo
Date: 2025-03-10 00:00:00
Proceeding: CVPR 2025 1
Tasks: Audio Generation, Quantization

Video-to-audio generation is essential for synthesizing realistic audio tracks that synchronize effectively with silent videos. Following the perspective of extracting essential signals from videos that can precisely control the mature text-to-audio generative diffusion models, this paper presents how to balance the representation of mel-spectrograms in terms of completeness and complexity through a new approach called Mel Quantization-Continuum Decomposition (Mel-QCD). We decompose the mel-spectrogram into three distinct types of signals, employing quantization or continuity to them, we can effectively predict them from video by a devised video-to-all (V2X) predictor. Then, the predicted signals are recomposed and fed into a ControlNet, along with a textual inversion design, to control the audio generation process. Our proposed Mel-QCD method demonstrates state-of-the-art performance across eight metrics, evaluating dimensions such as quality, synchronization, and semantic consistency. Our codes and demos will be released at \href{Website}{https://wjc2830.github.io/MelQCD/}.
Automatic Speech Recognition for Non-Native English: Accuracy and Disfluency Handling
Authors: Michael McGuire
Date: 2025-03-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic speech recognition (ASR) has been an essential component of computer assisted language learning (CALL) and computer assisted language testing (CALT) for many years. As this technology continues to develop rapidly, it is important to evaluate the accuracy of current ASR systems for language learning applications. This study assesses five cutting-edge ASR systems' recognition of non-native accented English speech using recordings from the L2-ARCTIC corpus, featuring speakers from six different L1 backgrounds (Arabic, Chinese, Hindi, Korean, Spanish, and Vietnamese), in the form of both read and spontaneous speech. The read speech consisted of 2,400 single sentence recordings from 24 speakers, while the spontaneous speech included narrative recordings from 22 speakers. Results showed that for read speech, Whisper and AssemblyAI achieved the best accuracy with mean Match Error Rates (MER) of 0.054 and 0.056 respectively, approaching human-level accuracy. For spontaneous speech, RevAI performed best with a mean MER of 0.063. The study also examined how each system handled disfluencies such as filler words, repetitions, and revisions, finding significant variation in performance across systems and disfluency types. While processing speed varied considerably between systems, longer processing times did not necessarily correlate with better accuracy. By detailing the performance of several of the most recent, widely-available ASR systems on non-native English speech, this study aims to help language instructors and researchers understand the strengths and weaknesses of each system and identify which may be suitable for specific use cases.
Multimodal Emotion Recognition and Sentiment Analysis in Multi-Party Conversation Contexts
Authors: Aref Farhadipour, Hossein Ranjbar, Masoumeh Chapariniya, Teodora Vukovic, Sarah Ebling, Volker Dellwo
Date: 2025-03-09 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition, Sentiment Analysis

Emotion recognition and sentiment analysis are pivotal tasks in speech and language processing, particularly in real-world scenarios involving multi-party, conversational data. This paper presents a multimodal approach to tackle these challenges on a well-known dataset. We propose a system that integrates four key modalities/channels using pre-trained models: RoBERTa for text, Wav2Vec2 for speech, a proposed FacialNet for facial expressions, and a CNN+Transformer architecture trained from scratch for video analysis. Feature embeddings from each modality are concatenated to form a multimodal vector, which is then used to predict emotion and sentiment labels. The multimodal system demonstrates superior performance compared to unimodal approaches, achieving an accuracy of 66.36% for emotion recognition and 72.15% for sentiment analysis.
Infant Cry Detection Using Causal Temporal Representation
Authors: Minghao Fu, Danning Li, Aryan Gadhiya, Benjamin Lambright, Mohamed Alowais, Mohab Bahnassy, Saad El Dine Elletter, Hawau Olamide Toyin, Haiyan Jiang, Kun Zhang, Hanan Aldarmaki
Date: 2025-03-08 00:00:00
Tasks: Event Detection

This paper addresses a major challenge in acoustic event detection, in particular infant cry detection in the presence of other sounds and background noises: the lack of precise annotated data. We present two contributions for supervised and unsupervised infant cry detection. The first is an annotated dataset for cry segmentation, which enables supervised models to achieve state-of-the-art performance. Additionally, we propose a novel unsupervised method, Causal Representation Spare Transition Clustering (CRSTC), based on causal temporal representation, which helps address the issue of data scarcity more generally. By integrating the detected cry segments, we significantly improve the performance of downstream infant cry classification, highlighting the potential of this approach for infant care applications.
Multi-modal expressive personality recognition in data non-ideal audiovisual based on multi-scale feature enhancement and modal augment
Authors: Weixuan Kong, Jinpeng Yu, Zijun Li, Hanwei Liu, Jiqing Qu, Hui Xiao, Xuefeng Li
Date: 2025-03-08 00:00:00

Automatic personality recognition is a research hotspot in the intersection of computer science and psychology, and in human-computer interaction, personalised has a wide range of applications services and other scenarios. In this paper, an end-to-end multimodal performance personality is established for both visual and auditory modal datarecognition network , and the through feature-level fusion , which effectively of the two modalities is carried out the cross-attention mechanismfuses the features of the two modal data; and a is proposed multiscale feature enhancement modalitiesmodule , which enhances for visual and auditory boththe expression of the information of effective the features and suppresses the interference of the redundant information. In addition, during the training process, this paper proposes a modal enhancement training strategy to simulate non-ideal such as modal loss and noise interferencedata situations , which enhances the adaptability ofand the model to non-ideal data scenarios improves the robustness of the model. Experimental results show that the method proposed in this paper is able to achieve an average Big Five personality accuracy of , which outperforms existing 0.916 on the personality analysis dataset ChaLearn First Impressionother methods based on audiovisual and audio-visual both modalities. The ablation experiments also validate our proposed , respectivelythe contribution of module and modality enhancement strategy to the model performance. Finally, we simulate in the inference phase multi-scale feature enhancement six non-ideal data scenarios to verify the modal enhancement strategy's improvement in model robustness.
Audio-to-Image Encoding for Improved Voice Characteristic Detection Using Deep Convolutional Neural Networks
Authors: Youness Atif
Date: 2025-03-07 00:00:00
Tasks: Speaker Recognition

This paper introduces a novel audio-to-image encoding framework that integrates multiple dimensions of voice characteristics into a single RGB image for speaker recognition. In this method, the green channel encodes raw audio data, the red channel embeds statistical descriptors of the voice signal (including key metrics such as median and mean values for fundamental frequency, spectral centroid, bandwidth, rolloff, zero-crossing rate, MFCCs, RMS energy, spectral flatness, spectral contrast, chroma, and harmonic-to-noise ratio), and the blue channel comprises subframes representing these features in a spatially organized format. A deep convolutional neural network trained on these composite images achieves 98% accuracy in speaker classification across two speakers, suggesting that this integrated multi-channel representation can provide a more discriminative input for voice recognition tasks.
S2S-Arena, Evaluating Speech2Speech Protocols on Instruction Following with Paralinguistic Information
Authors: Feng Jiang, Zhiyu Lin, Fan Bu, Yuhao Du, Benyou Wang, Haizhou Li
Date: 2025-03-07 00:00:00
Tasks: Instruction Following

The rapid development of large language models (LLMs) has brought significant attention to speech models, particularly recent progress in speech2speech protocols supporting speech input and output. However, the existing benchmarks adopt automatic text-based evaluators for evaluating the instruction following ability of these models lack consideration for paralinguistic information in both speech understanding and generation. To address these issues, we introduce S2S-Arena, a novel arena-style S2S benchmark that evaluates instruction-following capabilities with paralinguistic information in both speech-in and speech-out across real-world tasks. We design 154 samples that fused TTS and live recordings in four domains with 21 tasks and manually evaluate existing popular speech models in an arena-style manner. The experimental results show that: (1) in addition to the superior performance of GPT-4o, the speech model of cascaded ASR, LLM, and TTS outperforms the jointly trained model after text-speech alignment in speech2speech protocols; (2) considering paralinguistic information, the knowledgeability of the speech model mainly depends on the LLM backbone, and the multilingual support of that is limited by the speech module; (3) excellent speech models can already understand the paralinguistic information in speech input, but generating appropriate audio with paralinguistic information is still a challenge.
From Voice to Safety: Language AI Powered Pilot-ATC Communication Understanding for Airport Surface Movement Collision Risk Assessment
Authors: Yutian Pang, Andrew Paul Kendall, Alex Porcayo, Mariah Barsotti, Anahita Jain, John-Paul Clarke
Date: 2025-03-06 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, speech-recognition, Speech Recognition

This work integrates language AI-based voice communication understanding with collision risk assessment. The proposed framework consists of two major parts, (a) Automatic Speech Recognition (ASR); (b) surface collision risk modeling. ASR module generates information tables by processing voice communication transcripts, which serve as references for producing potential taxi plans and calculating the surface movement collision risk. For ASR, we collect and annotate our own Named Entity Recognition (NER) dataset based on open-sourced video recordings and safety investigation reports. Additionally, we refer to FAA Order JO 7110.65W and FAA Order JO 7340.2N to get the list of heuristic rules and phase contractions of communication between the pilot and the Air Traffic Controller (ATCo) used in daily aviation operations. Then, we propose the novel ATC Rule-Enhanced NER method, which integrates the heuristic rules into the model training and inference stages, resulting into hybrid rule-based NER model. We show the effectiveness of this hybrid approach by comparing different setups with different token-level embedding models. For the risk modeling, we adopt the node-link airport layout graph from NASA FACET and model the aircraft taxi speed at each link as a log-normal distribution and derive the total taxi time distribution. Then, we propose a spatiotemporal formulation of the risk probability of two aircraft moving across potential collision nodes during ground movement. We show the effectiveness of our approach by simulating two case studies, (a) the Henada airport runway collision accident happened in January 2024; (b) the KATL taxiway collision happened in September 2024. We show that, by understanding the pilot-ATC communication transcripts and analyzing surface movement patterns, the proposed model improves airport safety by providing risk assessment in time.
Scaling Rich Style-Prompted Text-to-Speech Datasets
Authors: Anuj Diwan, Zhisheng Zheng, David Harwath, Eunsol Choi
Date: 2025-03-06 00:00:00
Tasks: Language Modeling, Language Modelling, TAG, text-to-speech, Text to Speech

We introduce Paralinguistic Speech Captions (ParaSpeechCaps), a large-scale dataset that annotates speech utterances with rich style captions. While rich abstract tags (e.g. guttural, nasal, pained) have been explored in small-scale human-annotated datasets, existing large-scale datasets only cover basic tags (e.g. low-pitched, slow, loud). We combine off-the-shelf text and speech embedders, classifiers and an audio language model to automatically scale rich tag annotations for the first time. ParaSpeechCaps covers a total of 59 style tags, including both speaker-level intrinsic tags and utterance-level situational tags. It consists of 342 hours of human-labelled data (PSC-Base) and 2427 hours of automatically annotated data (PSC-Scaled). We finetune Parler-TTS, an open-source style-prompted TTS model, on ParaSpeechCaps, and achieve improved style consistency (+7.9% Consistency MOS) and speech quality (+15.5% Naturalness MOS) over the best performing baseline that combines existing rich style tag datasets. We ablate several of our dataset design choices to lay the foundation for future work in this space. Our dataset, models and code are released at https://github.com/ajd12342/paraspeechcaps .
Self-Supervised Models for Phoneme Recognition: Applications in Children's Speech for Reading Learning
Authors: Lucas Block Medin, Thomas Pellegrini, Lucile Gelin
Date: 2025-03-06 00:00:00
Tasks: Phoneme Recognition, Self-Supervised Learning, speech-recognition, Speech Recognition

Child speech recognition is still an underdeveloped area of research due to the lack of data (especially on non-English languages) and the specific difficulties of this task. Having explored various architectures for child speech recognition in previous work, in this article we tackle recent self-supervised models. We first compare wav2vec 2.0, HuBERT and WavLM models adapted to phoneme recognition in French child speech, and continue our experiments with the best of them, WavLM base+. We then further adapt it by unfreezing its transformer blocks during fine-tuning on child speech, which greatly improves its performance and makes it significantly outperform our base model, a Transformer+CTC. Finally, we study in detail the behaviour of these two models under the real conditions of our application, and show that WavLM base+ is more robust to various reading tasks and noise levels. Index Terms: speech recognition, child speech, self-supervised learning
TAIL: Text-Audio Incremental Learning
Authors: Yingfei Sun, Xu Gu, Wei Ji, Hanbin Zhao, Hao Fei, Yifang Yin, Roger Zimmermann
Date: 2025-03-06 00:00:00
Tasks: AudioCaps, Incremental Learning, text similarity

Many studies combine text and audio to capture multi-modal information but they overlook the model's generalization ability on new datasets. Introducing new datasets may affect the feature space of the original dataset, leading to catastrophic forgetting. Meanwhile, large model parameters can significantly impact training performance. To address these limitations, we introduce a novel task called Text-Audio Incremental Learning (TAIL) task for text-audio retrieval, and propose a new method, PTAT, Prompt Tuning for Audio-Text incremental learning. This method utilizes prompt tuning to optimize the model parameters while incorporating an audio-text similarity and feature distillation module to effectively mitigate catastrophic forgetting. We benchmark our method and previous incremental learning methods on AudioCaps, Clotho, BBC Sound Effects and Audioset datasets, and our method outperforms previous methods significantly, particularly demonstrating stronger resistance to forgetting on older datasets. Compared to the full-parameters Finetune (Sequential) method, our model only requires 2.42\% of its parameters, achieving 4.46\% higher performance.
Frequency-Based Alignment of EEG and Audio Signals Using Contrastive Learning and SincNet for Auditory Attention Detection
Authors: Yuan Liao, Yuhong Zhang, Qiushi Han, Yuhang Yang, Weiwei Ding, Yuzhe Gu, Hengxin Yang, Liya Huang
Date: 2025-03-06 00:00:00
Tasks: Contrastive Learning, EEG

Humans exhibit a remarkable ability to focus auditory attention in complex acoustic environments, such as cocktail parties. Auditory attention detection (AAD) aims to identify the attended speaker by analyzing brain signals, such as electroencephalography (EEG) data. Existing AAD algorithms often leverage deep learning's powerful nonlinear modeling capabilities, few consider the neural mechanisms underlying auditory processing in the brain. In this paper, we propose SincAlignNet, a novel network based on an improved SincNet and contrastive learning, designed to align audio and EEG features for auditory attention detection. The SincNet component simulates the brain's processing of audio during auditory attention, while contrastive learning guides the model to learn the relationship between EEG signals and attended speech. During inference, we calculate the cosine similarity between EEG and audio features and also explore direct inference of the attended speaker using EEG data. Cross-trial evaluations results demonstrate that SincAlignNet outperforms state-of-the-art AAD methods on two publicly available datasets, KUL and DTU, achieving average accuracies of 78.3% and 92.2%, respectively, with a 1-second decision window. The model exhibits strong interpretability, revealing that the left and right temporal lobes are more active during both male and female speaker scenarios. Furthermore, we found that using data from only six electrodes near the temporal lobes maintains similar or even better performance compared to using 64 electrodes. These findings indicate that efficient low-density EEG online decoding is achievable, marking an important step toward the practical implementation of neuro-guided hearing aids in real-world applications. Code is available at: https://github.com/LiaoEuan/SincAlignNet.
Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities
Authors: Sreyan Ghosh, Zhifeng Kong, Sonal Kumar, S Sakshi, Jaehyeon Kim, Wei Ping, Rafael Valle, Dinesh Manocha, Bryan Catanzaro
Date: 2025-03-06 00:00:00
Tasks: Audio captioning, Language Modeling, Language Modelling, Question Answering, Small Language Model

Understanding and reasoning over non-speech sounds and music are crucial for both humans and AI agents to interact effectively with their environments. In this paper, we introduce Audio Flamingo 2 (AF2), an Audio-Language Model (ALM) with advanced audio understanding and reasoning capabilities. AF2 leverages (i) a custom CLAP model, (ii) synthetic Audio QA data for fine-grained audio reasoning, and (iii) a multi-stage curriculum learning strategy. AF2 achieves state-of-the-art performance with only a 3B parameter small language model, surpassing large open-source and proprietary models across over 20 benchmarks. Next, for the first time, we extend audio understanding to long audio segments (30 secs to 5 mins) and propose LongAudio, a large and novel dataset for training ALMs on long audio captioning and question-answering tasks. Fine-tuning AF2 on LongAudio leads to exceptional performance on our proposed LongAudioBench, an expert annotated benchmark for evaluating ALMs on long audio understanding capabilities. We conduct extensive ablation studies to confirm the efficacy of our approach. Project Website: https://research.nvidia.com/labs/adlr/AF2/.
VoiceGRPO: Modern MoE Transformers with Group Relative Policy Optimization GRPO for AI Voice Health Care Applications on Voice Pathology Detection
Authors: Enkhtogtokh Togootogtokh, Christian Klasen
Date: 2025-03-05 00:00:00
Tasks: Diagnostic, Mixture-of-Experts, Voice pathology detection

This research introduces a novel AI techniques as Mixture-of-Experts Transformers with Group Relative Policy Optimization (GRPO) for voice health care applications on voice pathology detection. With the architectural innovations, we adopt advanced training paradigms inspired by reinforcement learning, namely Proximal Policy Optimization (PPO) and Group-wise Regularized Policy Optimization (GRPO), to enhance model stability and performance. Experiments conducted on a synthetically generated voice pathology dataset demonstrate that our proposed models significantly improve diagnostic accuracy, F1 score, and ROC-AUC compared to conventional approaches. These findings underscore the potential of integrating transformer architectures with novel training strategies to advance automated voice pathology detection and ultimately contribute to more effective healthcare delivery. The code we used to train and evaluate our models is available at https://github.com/enkhtogtokh/voicegrpo