pwc-archive/papers-with-abstracts huggingface dataset:
EMO-Codec: An In-Depth Look at Emotion Preservation capacity of Legacy and Neural Codec Models With Subjective and Objective Evaluations
Authors: Wenze Ren, Yi-Cheng Lin, Huang-Cheng Chou, Haibin Wu, Yi-Chiao Wu, Chi-Chun Lee, Hung-Yi Lee, Yu Tsao
Date: 2024-07-22 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

The neural codec model reduces speech data transmission delay and serves as the foundational tokenizer for speech language models (speech LMs). Preserving emotional information in codecs is crucial for effective communication and context understanding. However, there is a lack of studies on emotion loss in existing codecs. This paper evaluates neural and legacy codecs using subjective and objective methods on emotion datasets like IEMOCAP. Our study identifies which codecs best preserve emotional information under various bitrate scenarios. We found that training codec models with both English and Chinese data had limited success in retaining emotional information in Chinese. Additionally, resynthesizing speech through these codecs degrades the performance of speech emotion recognition (SER), particularly for emotions like sadness, depression, fear, and disgust. Human listening tests confirmed these findings. This work guides future speech technology developments to ensure new codecs maintain the integrity of emotional information in speech.
Can all variations within the unified mask-based beamformer framework achieve identical peak extraction performance?
Authors: Atsuo Hiroe, Katsutoshi Itoyama, Kazuhiro Nakadai
Date: 2024-07-22 00:00:00
Tasks: All, Target Sound Extraction

This study investigates mask-based beamformers (BFs), which estimate filters for target sound extraction (TSE) using time-frequency masks. Although multiple mask-based BFs have been proposed, no consensus has been reached on which one offers the best target-extraction performance. Previously, we found that maximum signal-to-noise ratio and minimum mean square error (MSE) BFs can achieve the same extraction performance as the theoretical upper-bound performance, with each BF containing a different optimal mask. However, two issues remained unsolved: only two BFs were covered, excluding the minimum variance distortionless response BF, and ideal scaling (IS) was employed to ideally adjust the output scale, which is not applicable to realistic scenarios. To address these issues, this study proposes a unified framework for mask-based BFs comprising two processes: filter estimation that can cover all possible BFs and scaling applicable to realistic scenarios by employing a mask to generate a scaling reference. Based on the operators and covariance matrices used in BF formulas, all possible BFs can be classified into 12 variations, including two new ones. Optimal masks for both processes are obtained by minimizing the MSE between the target and BF output. The experimental results using the CHiME-4 dataset suggested that 1) all 12 variations can achieve the theoretical upper-bound performance, and 2) mask-based scaling can behave like IS, even when constraining the temporal mean of a non-negative mask to one. These results can be explained by considering the practical parameter count of the masks. These findings contribute to 1) designing a TSE system, 2) improving scaling accuracy through mask-based scaling, and 3) estimating the extraction performance of a BF.
Explainability Paths for Sustained Artistic Practice with AI
Authors: Austin Tecks, Thomas Peschlow, Gabriel Vigliensoni
Date: 2024-07-21 00:00:00

The development of AI-driven generative audio mirrors broader AI trends, often prioritizing immediate accessibility at the expense of explainability. Consequently, integrating such tools into sustained artistic practice remains a significant challenge. In this paper, we explore several paths to improve explainability, drawing primarily from our research-creation practice in training and implementing generative audio models. As practical provisions for improved explainability, we highlight human agency over training materials, the viability of small-scale datasets, the facilitation of the iterative creative process, and the integration of interactive machine learning as a mapping tool. Importantly, these steps aim to enhance human agency over generative AI systems not only during model inference, but also when curating and preprocessing training data as well as during the training phase of models.
Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning
Authors: Shuai Wang, Zhengyang Chen, Kong Aik Lee, Yanmin Qian, Haizhou Li
Date: 2024-07-21 00:00:00
Tasks: Representation Learning, Self-Supervised Learning, speaker-diarization, Speaker Diarization, Speaker Recognition, Speech Synthesis, Target Speaker Extraction

Speaker individuality information is among the most critical elements within speech signals. By thoroughly and accurately modeling this information, it can be utilized in various intelligent speech applications, such as speaker recognition, speaker diarization, speech synthesis, and target speaker extraction. In this overview, we present a comprehensive review of neural approaches to speaker representation learning from both theoretical and practical perspectives. Theoretically, we discuss speaker encoders ranging from supervised to self-supervised learning algorithms, standalone models to large pretrained models, pure speaker embedding learning to joint optimization with downstream tasks, and efforts toward interpretability. Practically, we systematically examine approaches for robustness and effectiveness, introduce and compare various open-source toolkits in the field. Through the systematic and comprehensive review of the relevant literature, research activities, and resources, we provide a clear reference for researchers in the speaker characterization and modeling field, as well as for those who wish to apply speaker modeling techniques to specific downstream tasks.
MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music Generation
Authors: Yun-Han Lan, Wen-Yi Hsiao, Hao-Chung Cheng, Yi-Hsuan Yang
Date: 2024-07-21 00:00:00
Tasks: Diversity, Music Generation, Rhythm, Text-to-Music Generation

Existing text-to-music models can produce high-quality audio with great diversity. However, textual prompts alone cannot precisely control temporal musical features such as chords and rhythm of the generated music. To address this challenge, we introduce MusiConGen, a temporally-conditioned Transformer-based text-to-music model that builds upon the pretrained MusicGen framework. Our innovation lies in an efficient finetuning mechanism, tailored for consumer-grade GPUs, that integrates automatically-extracted rhythm and chords as the condition signal. During inference, the condition can either be musical features extracted from a reference audio signal, or be user-defined symbolic chord sequence, BPM, and textual prompts. Our performance evaluation on two datasets -- one derived from extracted features and the other from user-created inputs -- demonstrates that MusiConGen can generate realistic backing track music that aligns well with the specified conditions. We open-source the code and model checkpoints, and provide audio examples online, https://musicongen.github.io/musicongen_demo/.
Conversational Rubert for Detecting Competitive Interruptions in ASR-Transcribed Dialogues
Authors: Dmitrii Galimzianov, Viacheslav Vyshegorodtsev
Date: 2024-07-20 00:00:00
Tasks: Speech Interruption Detection

Interruption in a dialogue occurs when the listener begins their speech before the current speaker finishes speaking. Interruptions can be broadly divided into two groups: cooperative (when the listener wants to support the speaker), and competitive (when the listener tries to take control of the conversation against the speaker's will). A system that automatically classifies interruptions can be used in call centers, specifically in the tasks of customer satisfaction monitoring and agent monitoring. In this study, we developed a text-based interruption classification model by preparing an in-house dataset consisting of ASR-transcribed customer support telephone dialogues in Russian. We fine-tuned Conversational RuBERT on our dataset and optimized hyperparameters, and the model performed well. With further improvements, the proposed model can be applied to automatic monitoring systems.
Towards Realistic Emotional Voice Conversion using Controllable Emotional Intensity
Authors: Tianhua Qi, Shiyan Wang, Cheng Lu, Yan Zhao, Yuan Zong, Wenming Zheng
Date: 2024-07-20 00:00:00
Tasks: Diversity, Rhythm, Voice Conversion

Realistic emotional voice conversion (EVC) aims to enhance emotional diversity of converted audios, making the synthesized voices more authentic and natural. To this end, we propose Emotional Intensity-aware Network (EINet), dynamically adjusting intonation and rhythm by incorporating controllable emotional intensity. To better capture nuances in emotional intensity, we go beyond mere distance measurements among acoustic features. Instead, an emotion evaluator is utilized to precisely quantify speaker's emotional state. By employing an intensity mapper, intensity pseudo-labels are obtained to bridge the gap between emotional speech intensity modeling and run-time conversion. To ensure high speech quality while retaining controllability, an emotion renderer is used for combining linguistic features smoothly with manipulated emotional features at frame level. Furthermore, we employ a duration predictor to facilitate adaptive prediction of rhythm changes condition on specifying intensity value. Experimental results show EINet's superior performance in naturalness and diversity of emotional expression compared to state-of-the-art EVC methods.
Composer's Assistant 2: Interactive Multi-Track MIDI Infilling with Fine-Grained User Control
Authors: Martin E. Malandro
Date: 2024-07-19 00:00:00

We introduce Composer's Assistant 2, a system for interactive human-computer composition in the REAPER digital audio workstation. Our work upgrades the Composer's Assistant system (which performs multi-track infilling of symbolic music at the track-measure level) with a wide range of new controls to give users fine-grained control over the system's outputs. Controls introduced in this work include two types of rhythmic conditioning controls, horizontal and vertical note onset density controls, several types of pitch controls, and a rhythmic interest control. We train a T5-like transformer model to implement these controls and to serve as the backbone of our system. With these controls, we achieve a dramatic improvement in objective metrics over the original system. We also study how well our model understands the meaning of our controls, and we conduct a listening study that does not find a significant difference between real music and music composed in a co-creative fashion with our system. We release our complete system, consisting of source code, pretrained models, and REAPER scripts.
Topology-Independent GEVD-Based Distributed Adaptive Node-Specific Signal Estimation in Ad-Hoc Wireless Acoustic Sensor Networks
Authors: Paul Didier, Toon van Waterschoot, Marc Moonen
Date: 2024-07-19 00:00:00

A low-rank approximation-based version of the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm is introduced, using a generalized eigenvalue decomposition (GEVD) for application in ad-hoc wireless acoustic sensor networks. This TI-GEVD-DANSE algorithm as well as the original TI-DANSE algorithm exhibit a non-strict convergence, which can lead to numerical instability over time, particularly in scenarios where the estimation of accurate spatial covariance matrices is challenging. An adaptive filter coefficient normalization strategy is proposed to mitigate this issue and enable the stable performance of TI-(GEVD-)DANSE. The method is validated in numerical simulations including dynamic acoustic scenarios, demonstrating the importance of the additional normalization.
MSceneSpeech: A Multi-Scene Speech Dataset For Expressive Speech Synthesis
Authors: Qian Yang, Jialong Zuo, Zhe Su, Ziyue Jiang, Mingze Li, Zhou Zhao, Feiyang Chen, Zhefeng Wang, Baoxing Huai
Date: 2024-07-19 00:00:00
Tasks: Expressive Speech Synthesis, Speech Synthesis

We introduce an open source high-quality Mandarin TTS dataset MSceneSpeech (Multiple Scene Speech Dataset), which is intended to provide resources for expressive speech synthesis. MSceneSpeech comprises numerous audio recordings and texts performed and recorded according to daily life scenarios. Each scenario includes multiple speakers and a diverse range of prosodic styles, making it suitable for speech synthesis that entails multi-speaker style and prosody modeling. We have established a robust baseline, through the prompting mechanism, that can effectively synthesize speech characterized by both user-specific timbre and scene-specific prosody with arbitrary text input. The open source MSceneSpeech Dataset and audio samples of our baseline are available at https://speechai-demo.github.io/MSceneSpeech/.
Morse Code-Enabled Speech Recognition for Individuals with Visual and Hearing Impairments
Authors: Ritabrata Roy Choudhury
Date: 2024-07-07 00:00:00
Tasks: speech-recognition, Speech Recognition

The proposed model aims to develop a speech recognition technology for hearing, speech, or cognitively disabled people. All the available technology in the field of speech recognition doesn't come with an interface for communication for people with hearing, speech, or cognitive disabilities. The proposed model proposes the speech from the user, is transmitted to the speech recognition layer where it is converted into text and then that text is then transmitted to the morse code conversion layer where the morse code of the corresponding speech is given as the output. The accuracy of the model is completely dependent on speech recognition, as the morse code conversion is a process. The model is tested with recorded audio files with different parameters. The proposed model's WER and accuracy are both determined to be 10.18% and 89.82%, respectively.
Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition
Authors: Shujie Hu, Xurong Xie, Mengzhe Geng, Zengrui Jin, Jiajun Deng, Guinan Li, Yi Wang, Mingyu Cui, Tianzi Wang, Helen Meng, Xunying Liu
Date: 2024-07-03 00:00:00
Tasks: Alzheimer's Disease Detection, Self-Supervised Learning, speech-recognition, Speech Recognition

Self-supervised learning (SSL) based speech foundation models have been applied to a wide range of ASR tasks. However, their application to dysarthric and elderly speech via data-intensive parameter fine-tuning is confronted by in-domain data scarcity and mismatch. To this end, this paper explores a series of approaches to integrate domain fine-tuned SSL pre-trained models and their features into TDNN and Conformer ASR systems for dysarthric and elderly speech recognition. These include: a) input feature fusion between standard acoustic frontends and domain fine-tuned SSL speech representations; b) frame-level joint decoding between TDNN systems separately trained using standard acoustic features alone and those with additional domain fine-tuned SSL features; and c) multi-pass decoding involving the TDNN/Conformer system outputs to be rescored using domain fine-tuned pre-trained ASR models. In addition, fine-tuned SSL speech features are used in acoustic-to-articulatory (A2A) inversion to construct multi-modal ASR systems. Experiments are conducted on four tasks: the English UASpeech and TORGO dysarthric speech corpora; and the English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech datasets. The TDNN systems constructed by integrating domain-adapted HuBERT, wav2vec2-conformer or multi-lingual XLSR models and their features consistently outperform the standalone fine-tuned SSL pre-trained models. These systems produced statistically significant WER or CER reductions of 6.53%, 1.90%, 2.04% and 7.97% absolute (24.10%, 23.84%, 10.14% and 31.39% relative) on the four tasks respectively. Consistent improvements in Alzheimer's Disease detection accuracy are also obtained using the DementiaBank Pitt elderly speech recognition outputs.
From Modular to End-to-End Speaker Diarization
Authors: Federico Landini
Date: 2024-06-27 00:00:00
Tasks: speaker-diarization, Speaker Diarization

Speaker diarization is usually referred to as the task that determines ``who spoke when'' in a recording. Until a few years ago, all competitive approaches were modular. Systems based on this framework reached state-of-the-art performance in most scenarios but had major difficulties dealing with overlapped speech. More recently, the advent of end-to-end models, capable of dealing with all aspects of speaker diarization with a single model and better performing regarding overlapped speech, has brought high levels of attention. This thesis is framed during a period of co-existence of these two trends. We describe a system based on a Bayesian hidden Markov model used to cluster x-vectors (speaker embeddings obtained with a neural network), known as VBx, which has shown remarkable performance on different datasets and challenges. We comment on its advantages and limitations and evaluate results on different relevant corpora. Then, we move towards end-to-end neural diarization (EEND) methods. Due to the need for large training sets for training these models and the lack of manually annotated diarization data in sufficient quantities, the compromise solution consists in generating training data artificially. We describe an approach for generating synthetic data which resembles real conversations in terms of speaker turns and overlaps. We show how this method generating ``simulated conversations'' allows for better performance than using a previously proposed method for creating ``simulated mixtures'' when training the popular EEND with encoder-decoder attractors (EEND-EDA). We also propose a new EEND-based model, which we call DiaPer, and show that it can perform better than EEND-EDA, especially when dealing with many speakers and handling overlapped speech. Finally, we compare both VBx-based and DiaPer systems on a wide variety of corpora and comment on the advantages of each technique.
Exploring Gender-Specific Speech Patterns in Automatic Suicide Risk Assessment
Authors: Maurice Gerczuk, Shahin Amiriparian, Justina Lutz, Wolfgang Strube, Irina Papazova, Alkomiet Hasan, Björn W. Schuller
Date: 2024-06-26 00:00:00

In emergency medicine, timely intervention for patients at risk of suicide is often hindered by delayed access to specialised psychiatric care. To bridge this gap, we introduce a speech-based approach for automatic suicide risk assessment. Our study involves a novel dataset comprising speech recordings of 20 patients who read neutral texts. We extract four speech representations encompassing interpretable and deep features. Further, we explore the impact of gender-based modelling and phrase-level normalisation. By applying gender-exclusive modelling, features extracted from an emotion fine-tuned wav2vec2.0 model can be utilised to discriminate high- from low- suicide risk with a balanced accuracy of 81%. Finally, our analysis reveals a discrepancy in the relationship of speech characteristics and suicide risk between female and male subjects. For men in our dataset, suicide risk increases together with agitation while voice characteristics of female subjects point the other way.
VideoGameBunny: Towards vision assistants for video games
Authors: Mohammad Reza Taesiri, Cor-Paul Bezemer
Date: 2024-07-21 00:00:00
Tasks: Image Captioning, Scene Understanding

Large multimodal models (LMMs) hold substantial promise across various domains, from personal assistance in daily tasks to sophisticated applications like medical diagnostics. However, their capabilities have limitations in the video game domain, such as challenges with scene understanding, hallucinations, and inaccurate descriptions of video game content, especially in open-source models. This paper describes the development of VideoGameBunny, a LLaVA-style model based on Bunny, specifically tailored for understanding images from video games. We release intermediate checkpoints, training logs, and an extensive dataset comprising 185,259 video game images from 413 titles, along with 389,565 image-instruction pairs that include image captions, question-answer pairs, and a JSON representation of 16 elements of 136,974 images. Our experiments show that our high quality game-related data has the potential to make a relatively small model outperform the much larger state-of-the-art model LLaVa-1.6-34b (which has more than 4x the number of parameters). Our study paves the way for future research in video game understanding on tasks such as playing, commentary, and debugging. Code and data are available at https://videogamebunny.github.io/
Detecting and Characterising Mobile App Metamorphosis in Google Play Store
Authors: D. Denipitiyage, B. Silva, K. Gunathilaka, S. Seneviratne, A. Mahanti, A. Seneviratne, S. Chawla
Date: 2024-07-19 00:00:00

App markets have evolved into highly competitive and dynamic environments for developers. While the traditional app life cycle involves incremental updates for feature enhancements and issue resolution, some apps deviate from this norm by undergoing significant transformations in their use cases or market positioning. We define this previously unstudied phenomenon as 'app metamorphosis'. In this paper, we propose a novel and efficient multi-modal search methodology to identify apps undergoing metamorphosis and apply it to analyse two snapshots of the Google Play Store taken five years apart. Our methodology uncovers various metamorphosis scenarios, including re-births, re-branding, re-purposing, and others, enabling comprehensive characterisation. Although these transformations may register as successful for app developers based on our defined success score metric (e.g., re-branded apps performing approximately 11.3% better than an average top app), we shed light on the concealed security and privacy risks that lurk within, potentially impacting even tech-savvy end-users.
Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social Balance
Authors: Marco Minici, Federico Cinus, Francesco Bonchi, Giuseppe Manco
Date: 2024-07-22 00:00:00

Signed Graph Neural Networks (SGNNs) have recently gained attention as an effective tool for several learning tasks on signed networks, i.e., graphs where edges have an associated polarity. One of these tasks is to predict the polarity of the links for which this information is missing, starting from the network structure and the other available polarities. However, when the available polarities are few and potentially noisy, such a task becomes challenging. In this work, we devise a semi-supervised learning framework that builds around the novel concept of \emph{multiscale social balance} to improve the prediction of link polarities in settings characterized by limited data quantity and quality. Our model-agnostic approach can seamlessly integrate with any SGNN architecture, dynamically reweighting the importance of each data sample while making strategic use of the structural information from unlabeled edges combined with social balance theory. Empirical validation demonstrates that our approach outperforms established baseline models, effectively addressing the limitations imposed by noisy and sparse data. This result underlines the benefits of incorporating multiscale social balance into SGNNs, opening new avenues for robust and accurate predictions in signed network analysis.