pwc-archive/papers-with-abstracts huggingface dataset:
A Study on Zero-shot Non-intrusive Speech Assessment using Large Language Models
Authors: Ryandhimas E. Zezario, Sabato M. Siniscalchi, Hsin-Min Wang, Yu Tsao
Date: 2024-09-16 00:00:00
Tasks: Automatic Speech Recognition, Prompt Engineering, speech-recognition, Speech Recognition

This work investigates two strategies for zero-shot non-intrusive speech assessment leveraging large language models. First, we explore the audio analysis capabilities of GPT-4o. Second, we propose GPT-Whisper, which uses Whisper as an audio-to-text module and evaluates the naturalness of text via targeted prompt engineering. We evaluate the assessment metrics predicted by GPT-4o and GPT-Whisper, examining their correlation with human-based quality and intelligibility assessments and the character error rate (CER) of automatic speech recognition. Experimental results show that GPT-4o alone is less effective for audio analysis, while GPT-Whisper achieves higher prediction accuracy, has moderate correlation with speech quality and intelligibility, and has higher correlation with CER. Compared to SpeechLMScore and DNSMOS, GPT-Whisper excels in intelligibility metrics, but performs slightly worse than SpeechLMScore in quality estimation. Furthermore, GPT-Whisper outperforms supervised non-intrusive models MOS-SSL and MTI-Net in Spearman's rank correlation for CER of Whisper. These findings validate GPT-Whisper's potential for zero-shot speech assessment without requiring additional training data.
Self-supervised Multimodal Speech Representations for the Assessment of Schizophrenia Symptoms
Authors: Gowtham Premananth, Carol Espy-Wilson
Date: 2024-09-15 00:00:00
Tasks: Multi-class Classification, Multi-Task Learning, Representation Learning

Multimodal schizophrenia assessment systems have gained traction over the last few years. This work introduces a schizophrenia assessment system to discern between prominent symptom classes of schizophrenia and predict an overall schizophrenia severity score. We develop a Vector Quantized Variational Auto-Encoder (VQ-VAE) based Multimodal Representation Learning (MRL) model to produce task-agnostic speech representations from vocal Tract Variables (TVs) and Facial Action Units (FAUs). These representations are then used in a Multi-Task Learning (MTL) based downstream prediction model to obtain class labels and an overall severity score. The proposed framework outperforms the previous works on the multi-class classification task across all evaluation metrics (Weighted F1 score, AUC-ROC score, and Weighted Accuracy). Additionally, it estimates the schizophrenia severity score, a task not addressed by earlier approaches.
Effective Pre-Training of Audio Transformers for Sound Event Detection
Authors: Florian Schmid, Tobias Morocutti, Francesco Foscarin, Jan Schlüter, Paul Primus, Gerhard Widmer
Date: 2024-09-14 00:00:00
Tasks: Data Augmentation, Event Detection, Knowledge Distillation, Sound Event Detection

We propose a pre-training pipeline for audio spectrogram transformers for frame-level sound event detection tasks. On top of common pre-training steps, we add a meticulously designed training routine on AudioSet frame-level annotations. This includes a balanced sampler, aggressive data augmentation, and ensemble knowledge distillation. For five transformers, we obtain a substantial performance improvement over previously available checkpoints both on AudioSet frame-level predictions and on frame-level sound event detection downstream tasks, confirming our pipeline's effectiveness. We publish the resulting checkpoints that researchers can directly fine-tune to build high-performance models for sound event detection tasks.
Target Speaker ASR with Whisper
Authors: Alexander Polok, Dominik Klement, Matthew Wiesner, Sanjeev Khudanpur, Jan Černocký, Lukáš Burget
Date: 2024-09-14 00:00:00
Tasks: Speech Separation

We propose a novel approach to enable the use of large, single-speaker ASR models, such as Whisper, for target speaker ASR. The key claim of this method is that it is much easier to model relative differences among speakers by learning to condition on frame-level diarization outputs than to learn the space of all speaker embeddings. We find that adding even a single bias term per diarization output type before the first transformer block can transform single-speaker ASR models into target-speaker ASR models. Our approach also supports speaker-attributed ASR by sequentially generating transcripts for each speaker in a diarization output. This simplified method outperforms baseline speech separation and diarization cascade by 12.9 % absolute ORC-WER on the NOTSOFAR-1 dataset.
Leveraging Self-Supervised Learning for Speaker Diarization
Authors: Jiangyu Han, Federico Landini, Johan Rohdin, Anna Silnova, Mireia Diez, Lukas Burget
Date: 2024-09-14 00:00:00
Tasks: Self-Supervised Learning, speaker-diarization, Speaker Diarization

End-to-end neural diarization has evolved considerably over the past few years, but data scarcity is still a major obstacle for further improvements. Self-supervised learning methods such as WavLM have shown promising performance on several downstream tasks, but their application on speaker diarization is somehow limited. In this work, we explore using WavLM to alleviate the problem of data scarcity for neural diarization training. We use the same pipeline as Pyannote and improve the local end-to-end neural diarization with WavLM and Conformer. Experiments on far-field AMI, AISHELL-4, and AliMeeting datasets show that our method substantially outperforms the Pyannote baseline and achieves new state-of-the-art results on AMI and AISHELL-4, respectively. In addition, by analyzing the system performance under different data quantity scenarios, we show that WavLM representations are much more robust against data scarcity than filterbank features, enabling less data hungry training strategies. Furthermore, we found that simulated data, usually used to train endto-end diarization models, does not help when using WavLM in our experiments. Additionally, we also evaluate our model on the recent CHiME8 NOTSOFAR-1 task where it achieves better performance than the Pyannote baseline. Our source code is publicly available at https://github.com/BUTSpeechFIT/DiariZen.
Language-Queried Target Sound Extraction Without Parallel Training Data
Authors: Hao Ma, Zhiyuan Peng, Xu Li, Yukai Li, Mingjie Shao, Qiuqiang Kong, Ju Liu
Date: 2024-09-14 00:00:00
Tasks: Language Modelling, Large Language Model, Retrieval, Target Sound Extraction

Language-queried target sound extraction (TSE) aims to extract specific sounds from mixtures based on language queries. Traditional fully-supervised training schemes require extensively annotated parallel audio-text data, which are labor-intensive. We introduce a parallel-data-free training scheme, requiring only unlabelled audio clips for TSE model training by utilizing the contrastive language-audio pre-trained model (CLAP). In a vanilla parallel-data-free training stage, target audio is encoded using the pre-trained CLAP audio encoder to form a condition embedding, while during testing, user language queries are encoded by CLAP text encoder as the condition embedding. This vanilla approach assumes perfect alignment between text and audio embeddings, which is unrealistic. Two major challenges arise from training-testing mismatch: the persistent modality gap between text and audio and the risk of overfitting due to the exposure of rich acoustic details in target audio embedding during training. To address this, we propose a retrieval-augmented strategy. Specifically, we create an embedding cache using audio captions generated by a large language model (LLM). During training, target audio embeddings retrieve text embeddings from this cache to use as condition embeddings, ensuring consistent modalities between training and testing and eliminating information leakage. Extensive experiment results show that our retrieval-augmented approach achieves consistent and notable performance improvements over existing state-of-the-art with better generalizability.
Channel Adaptation for Speaker Verification Using Optimal Transport with Pseudo Label
Authors: Wenhao Yang, Jianguo Wei, Wenhuan Lu, Lei LI, Xugang Lu
Date: 2024-09-14 00:00:00
Tasks: Domain Adaptation, Pseudo Label, Speaker Verification, Unsupervised Domain Adaptation

Domain gap often degrades the performance of speaker verification (SV) systems when the statistical distributions of training data and real-world test speech are mismatched. Channel variation, a primary factor causing this gap, is less addressed than other issues (e.g., noise). Although various domain adaptation algorithms could be applied to handle this domain gap problem, most algorithms could not take the complex distribution structure in domain alignment with discriminative learning. In this paper, we propose a novel unsupervised domain adaptation method, i.e., Joint Partial Optimal Transport with Pseudo Label (JPOT-PL), to alleviate the channel mismatch problem. Leveraging the geometric-aware distance metric of optimal transport in distribution alignment, we further design a pseudo label-based discriminative learning where the pseudo label can be regarded as a new type of soft speaker label derived from the optimal coupling. With the JPOT-PL, we carry out experiments on the SV channel adaptation task with VoxCeleb as the basis corpus. Experiments show our method reduces EER by over 10% compared with several state-of-the-art channel adaptation algorithms.
Integrated Multi-Level Knowledge Distillation for Enhanced Speaker Verification
Authors: Wenhao Yang, Jianguo Wei, Wenhuan Lu, Xugang Lu, Lei LI
Date: 2024-09-14 00:00:00
Tasks: Knowledge Distillation, Speaker Verification

Knowledge distillation (KD) is widely used in audio tasks, such as speaker verification (SV), by transferring knowledge from a well-trained large model (the teacher) to a smaller, more compact model (the student) for efficiency and portability. Existing KD methods for SV often mirror those used in image processing, focusing on approximating predicted probabilities and hidden representations. However, these methods fail to account for the multi-level temporal properties of speech audio. In this paper, we propose a novel KD method, i.e., Integrated Multi-level Knowledge Distillation (IML-KD), to transfer knowledge of various temporal-scale features of speech from a teacher model to a student model. In the IML-KD, temporal context information from the teacher model is integrated into novel Integrated Gradient-based input-sensitive representations from speech segments with various durations, and the student model is trained to infer these representations with multi-level alignment for the output. We conduct SV experiments on the VoxCeleb1 dataset to evaluate the proposed method. Experimental results demonstrate that IML-KD significantly enhances KD performance, reducing the Equal Error Rate (EER) by 5%.
E1 TTS: Simple and Fast Non-Autoregressive TTS
Authors: Zhijun Liu, Shuai Wang, Pengcheng Zhu, Mengxiao Bi, Haizhou Li
Date: 2024-09-14 00:00:00
Tasks: Denoising, text-to-speech, Text to Speech

This paper introduces Easy One-Step Text-to-Speech (E1 TTS), an efficient non-autoregressive zero-shot text-to-speech system based on denoising diffusion pretraining and distribution matching distillation. The training of E1 TTS is straightforward; it does not require explicit monotonic alignment between the text and audio pairs. The inference of E1 TTS is efficient, requiring only one neural network evaluation for each utterance. Despite its sampling efficiency, E1 TTS achieves naturalness and speaker similarity comparable to various strong baseline models. Audio samples are available at http://e1tts.github.io/ .
Improvements of Discriminative Feature Space Training for Anomalous Sound Detection in Unlabeled Conditions
Authors: Takuya Fujimura, Ibuki Kuroyanagi, Tomoki Toda
Date: 2024-09-14 00:00:00

In anomalous sound detection, the discriminative method has demonstrated superior performance. This approach constructs a discriminative feature space through the classification of the meta-information labels for normal sounds. This feature space reflects the differences in machine sounds and effectively captures anomalous sounds. However, its performance significantly degrades when the meta-information labels are missing. In this paper, we improve the performance of a discriminative method under unlabeled conditions by two approaches. First, we enhance the feature extractor to perform better under unlabeled conditions. Our enhanced feature extractor utilizes multi-resolution spectrograms with a new training strategy. Second, we propose various pseudo-labeling methods to effectively train the feature extractor. The experimental evaluations show that the proposed feature extractor and pseudo-labeling methods significantly improve performance under unlabeled conditions.
Improving Robustness of Diffusion-Based Zero-Shot Speech Synthesis via Stable Formant Generation
Authors: Changjin Han, Seokgi Lee, Gyuhyeon Nam, Gyeongsu Chae
Date: 2024-09-14 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

Diffusion models have achieved remarkable success in text-to-speech (TTS), even in zero-shot scenarios. Recent efforts aim to address the trade-off between inference speed and sound quality, often considered the primary drawback of diffusion models. However, we find a critical mispronunciation issue is being overlooked. Our preliminary study reveals the unstable pronunciation resulting from the diffusion process. Based on this observation, we introduce StableForm-TTS, a novel zero-shot speech synthesis framework designed to produce robust pronunciation while maintaining the advantages of diffusion modeling. By pioneering the adoption of source-filter theory in diffusion TTS, we propose an elaborate architecture for stable formant generation. Experimental results on unseen speakers show that our model outperforms the state-of-the-art method in terms of pronunciation accuracy and naturalness, with comparable speaker similarity. Moreover, our model demonstrates effective scalability as both data and model sizes increase. Audio samples are available online: https://deepbrainai-research.github.io/stableformtts/.
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech
Authors: Pan-Pan Jiang, Jimmy Tobin, Katrin Tomanek, Robert L. MacDonald, Katie Seaver, Richard Cave, Marilyn Ladewig, Rus Heywood, Jordan R. Green
Date: 2024-09-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Diversity, speech-recognition, Speech Recognition

Project Euphonia, a Google initiative, is dedicated to improving automatic speech recognition (ASR) of disordered speech. A central objective of the project is to create a large, high-quality, and diverse speech corpus. This report describes the project's latest advancements in data collection and annotation methodologies, such as expanding speaker diversity in the database, adding human-reviewed transcript corrections and audio quality tags to 350K (of the 1.2M total) audio recordings, and amassing a comprehensive set of metadata (including more than 40 speech characteristic labels) for over 75\% of the speakers in the database. We report on the impact of transcript corrections on our machine-learning (ML) research, inter-rater variability of assessments of disordered speech patterns, and our rationale for gathering speech metadata. We also consider the limitations of using automated off-the-shelf annotation methods for assessing disordered speech.
MambaFoley: Foley Sound Generation using Selective State-Space Models
Authors: Marco Furio Colombo, Francesca Ronchini, Luca Comanducci, Fabio Antonacci
Date: 2024-09-13 00:00:00
Tasks: Denoising, Mamba, State Space Models, valid

Recent advancements in deep learning have led to widespread use of techniques for audio content generation, notably employing Denoising Diffusion Probabilistic Models (DDPM) across various tasks. Among these, Foley Sound Synthesis is of particular interest for its role in applications for the creation of multimedia content. Given the temporal-dependent nature of sound, it is crucial to design generative models that can effectively handle the sequential modeling of audio samples. Selective State Space Models (SSMs) have recently been proposed as a valid alternative to previously proposed techniques, demonstrating competitive performance with lower computational complexity. In this paper, we introduce MambaFoley, a diffusion-based model that, to the best of our knowledge, is the first to leverage the recently proposed SSM known as Mamba for the Foley sound generation task. To evaluate the effectiveness of the proposed method, we compare it with a state-of-the-art Foley sound generative model using both objective and subjective analyses.
Why some audio signal short-time Fourier transform coefficients have nonuniform phase distributions
Authors: Stephen D. Voran
Date: 2024-09-13 00:00:00

The short-time Fourier transform (STFT) represents a window of audio samples as a set of complex coefficients. These are advantageously viewed as magnitudes and phases and the overall distribution of phases is very often assumed to be uniform. We show that when audio signal STFT phase distributions are analyzed per-frequency or per-magnitude range, they can be far from uniform. That is, the uniform phase distribution assumption obscures significant important details. We explain the significance of the nonuniform phase distributions and how they might be exploited, derive their source, and explain why the choice of the STFT window shape influences the nonuniformity of the resulting phase distributions.
Using Ear-EEG to Decode Auditory Attention in Multiple-speaker Environment
Authors: Haolin Zhu, Yujie Yan, Xiran Xu, Zhongshu Ge, Pei Tian, Xihong Wu, Jing Chen
Date: 2024-09-13 00:00:00
Tasks: EEG

Auditory Attention Decoding (AAD) can help to determine the identity of the attended speaker during an auditory selective attention task, by analyzing and processing measurements of electroencephalography (EEG) data. Most studies on AAD are based on scalp-EEG signals in two-speaker scenarios, which are far from real application. Ear-EEG has recently gained significant attention due to its motion tolerance and invisibility during data acquisition, making it easy to incorporate with other devices for applications. In this work, participants selectively attended to one of the four spatially separated speakers' speech in an anechoic room. The EEG data were concurrently collected from a scalp-EEG system and an ear-EEG system (cEEGrids). Temporal response functions (TRFs) and stimulus reconstruction (SR) were utilized using ear-EEG data. Results showed that the attended speech TRFs were stronger than each unattended speech and decoding accuracy was 41.3\% in the 60s (chance level of 25\%). To further investigate the impact of electrode placement and quantity, SR was utilized in both scalp-EEG and ear-EEG, revealing that while the number of electrodes had a minor effect, their positioning had a significant influence on the decoding accuracy. One kind of auditory spatial attention detection (ASAD) method, STAnet, was testified with this ear-EEG database, resulting in 93.1% in 1-second decoding window. The implementation code and database for our work are available on GitHub: https://github.com/zhl486/Ear_EEG_code.git and Zenodo: https://zenodo.org/records/10803261.
DualSep: A Light-weight dual-encoder convolutional recurrent network for real-time in-car speech separation
Authors: Ziqian Wang, Jiayao Sun, Zihan Zhang, Xingchen Li, Jie Liu, Lei Xie
Date: 2024-09-13 00:00:00
Tasks: CPU, Speech Separation

Advancements in deep learning and voice-activated technologies have driven the development of human-vehicle interaction. Distributed microphone arrays are widely used in in-car scenarios because they can accurately capture the voices of passengers from different speech zones. However, the increase in the number of audio channels, coupled with the limited computational resources and low latency requirements of in-car systems, presents challenges for in-car multi-channel speech separation. To migrate the problems, we propose a lightweight framework that cascades digital signal processing (DSP) and neural networks (NN). We utilize fixed beamforming (BF) to reduce computational costs and independent vector analysis (IVA) to provide spatial prior. We employ dual encoders for dual-branch modeling, with spatial encoder capturing spatial cues and spectral encoder preserving spectral information, facilitating spatial-spectral fusion. Our proposed system supports both streaming and non-streaming modes. Experimental results demonstrate the superiority of the proposed system across various metrics. With only 0.83M parameters and 0.39 real-time factor (RTF) on an Intel Core i7 (2.6GHz) CPU, it effectively separates speech into distinct speech zones. Our demos are available at https://honee-w.github.io/DualSep/.
Effective Integration of KAN for Keyword Spotting
Authors: Anfeng Xu, Biqiao Zhang, Shuyu Kong, Yiteng Huang, Zhaojun Yang, Sangeeta Srivastava, Ming Sun
Date: 2024-09-13 00:00:00
Tasks: Keyword Spotting, Kolmogorov-Arnold Networks

Keyword spotting (KWS) is an important speech processing component for smart devices with voice assistance capability. In this paper, we investigate if Kolmogorov-Arnold Networks (KAN) can be used to enhance the performance of KWS. We explore various approaches to integrate KAN for a model architecture based on 1D Convolutional Neural Networks (CNN). We find that KAN is effective at modeling high-level features in lower-dimensional spaces, resulting in improved KWS performance when integrated appropriately. The findings shed light on understanding KAN for speech processing tasks and on other modalities for future researchers.
Frequency Tracking Features for Data-Efficient Deep Siren Identification
Authors: Stefano Damiano, Thomas Dietzen, Toon van Waterschoot
Date: 2024-09-13 00:00:00
Tasks: Diversity, Domain Generalization

The identification of siren sounds in urban soundscapes is a crucial safety aspect for smart vehicles and has been widely addressed by means of neural networks that ensure robustness to both the diversity of siren signals and the strong and unstructured background noise characterizing traffic. Convolutional neural networks analyzing spectrogram features of incoming signals achieve state-of-the-art performance when enough training data capturing the diversity of the target acoustic scenes is available. In practice, data is usually limited and algorithms should be robust to adapt to unseen acoustic conditions without requiring extensive datasets for re-training. In this work, given the harmonic nature of siren signals, characterized by a periodically evolving fundamental frequency, we propose a low-complexity feature extraction method based on frequency tracking using a single-parameter adaptive notch filter. The features are then used to design a small-scale convolutional network suitable for training with limited data. The evaluation results indicate that the proposed model consistently outperforms the traditional spectrogram-based model when limited training data is available, achieves better cross-domain generalization and has a smaller size.