arXiv - EE - Audio and Speech Processing最新文献_第8页

Super Monotonic Alignment Search 超单调对齐搜索

arXiv - EE - Audio and Speech Processing Pub Date : 2024-09-12 DOI: arxiv-2409.07704

Junhyeok Lee, Hyeongju Kim

引用次数: 0

AudioBERT: Audio Knowledge Augmented Language Model AudioBERT：音频知识增强语言模型

arXiv - EE - Audio and Speech Processing Pub Date : 2024-09-12 DOI: arxiv-2409.08199

Hyunjong Ok, Suho Yoo, Jaeho Lee

引用次数: 0

Full-text Error Correction for Chinese Speech Recognition with Large Language Model 利用大语言模型为中文语音识别进行全文纠错

arXiv - EE - Audio and Speech Processing Pub Date : 2024-09-12 DOI: arxiv-2409.07790

Zhiyuan Tang, Dong Wang, Shen Huang, Shidong Shang

{"title":"Full-text Error Correction for Chinese Speech Recognition with Large Language Model","authors":"Zhiyuan Tang, Dong Wang, Shen Huang, Shidong Shang","doi":"arxiv-2409.07790","DOIUrl":"https://doi.org/arxiv-2409.07790","url":null,"abstract":"Large Language Models (LLMs) have demonstrated substantial potential for\u0000error correction in Automatic Speech Recognition (ASR). However, most research\u0000focuses on utterances from short-duration speech recordings, which are the\u0000predominant form of speech data for supervised ASR training. This paper\u0000investigates the effectiveness of LLMs for error correction in full-text\u0000generated by ASR systems from longer speech recordings, such as transcripts\u0000from podcasts, news broadcasts, and meetings. First, we develop a Chinese\u0000dataset for full-text error correction, named ChFT, utilizing a pipeline that\u0000involves text-to-speech synthesis, ASR, and error-correction pair extractor.\u0000This dataset enables us to correct errors across contexts, including both\u0000full-text and segment, and to address a broader range of error types, such as\u0000punctuation restoration and inverse text normalization, thus making the\u0000correction process comprehensive. Second, we fine-tune a pre-trained LLM on the\u0000constructed dataset using a diverse set of prompts and target formats, and\u0000evaluate its performance on full-text error correction. Specifically, we design\u0000prompts based on full-text and segment, considering various output formats,\u0000such as directly corrected text and JSON-based error-correction pairs. Through\u0000various test settings, including homogeneous, up-to-date, and hard test sets,\u0000we find that the fine-tuned LLMs perform well in the full-text setting with\u0000different prompts, each presenting its own strengths and weaknesses. This\u0000establishes a promising baseline for further research. The dataset is available\u0000on the website.","PeriodicalId":501284,"journal":{"name":"arXiv - EE - Audio and Speech Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Music auto-tagging in the long tail: A few-shot approach 长尾中的音乐自动标记：寥寥数语的方法

arXiv - EE - Audio and Speech Processing Pub Date : 2024-09-12 DOI: arxiv-2409.07730

T. Aleksandra Ma, Alexander Lerch

{"title":"Music auto-tagging in the long tail: A few-shot approach","authors":"T. Aleksandra Ma, Alexander Lerch","doi":"arxiv-2409.07730","DOIUrl":"https://doi.org/arxiv-2409.07730","url":null,"abstract":"In the realm of digital music, using tags to efficiently organize and\u0000retrieve music from extensive databases is crucial for music catalog owners.\u0000Human tagging by experts is labor-intensive but mostly accurate, whereas\u0000automatic tagging through supervised learning has approached satisfying\u0000accuracy but is restricted to a predefined set of training tags. Few-shot\u0000learning offers a viable solution to expand beyond this small set of predefined\u0000tags by enabling models to learn from only a few human-provided examples to\u0000understand tag meanings and subsequently apply these tags autonomously. We\u0000propose to integrate few-shot learning methodology into multi-label music\u0000auto-tagging by using features from pre-trained models as inputs to a\u0000lightweight linear classifier, also known as a linear probe. We investigate\u0000different popular pre-trained features, as well as different few-shot\u0000parametrizations with varying numbers of classes and samples per class. Our\u0000experiments demonstrate that a simple model with pre-trained features can\u0000achieve performance close to state-of-the-art models while using significantly\u0000less training data, such as 20 samples per tag. Additionally, our linear probe\u0000performs competitively with leading models when trained on the entire training\u0000dataset. The results show that this transfer learning-based few-shot approach\u0000could effectively address the issue of automatically assigning long-tail tags\u0000with only limited labeled data.","PeriodicalId":501284,"journal":{"name":"arXiv - EE - Audio and Speech Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Auto-Landmark: Acoustic Landmark Dataset and Open-Source Toolkit for Landmark Extraction 自动地标：声学地标数据集和地标提取开源工具包

arXiv - EE - Audio and Speech Processing Pub Date : 2024-09-12 DOI: arxiv-2409.07969

Xiangyu Zhang, Daijiao Liu, Tianyi Xiao, Cihan Xiao, Tuende Szalay, Mostafa Shahin, Beena Ahmed, Julien Epps

{"title":"Auto-Landmark: Acoustic Landmark Dataset and Open-Source Toolkit for Landmark Extraction","authors":"Xiangyu Zhang, Daijiao Liu, Tianyi Xiao, Cihan Xiao, Tuende Szalay, Mostafa Shahin, Beena Ahmed, Julien Epps","doi":"arxiv-2409.07969","DOIUrl":"https://doi.org/arxiv-2409.07969","url":null,"abstract":"In the speech signal, acoustic landmarks identify times when the acoustic\u0000manifestations of the linguistically motivated distinctive features are most\u0000salient. Acoustic landmarks have been widely applied in various domains,\u0000including speech recognition, speech depression detection, clinical analysis of\u0000speech abnormalities, and the detection of disordered speech. However, there is\u0000currently no dataset available that provides precise timing information for\u0000landmarks, which has been proven to be crucial for downstream applications\u0000involving landmarks. In this paper, we selected the most useful acoustic\u0000landmarks based on previous research and annotated the TIMIT dataset with them,\u0000based on a combination of phoneme boundary information and manual inspection.\u0000Moreover, previous landmark extraction tools were not open source or\u0000benchmarked, so to address this, we developed an open source Python-based\u0000landmark extraction tool and established a series of landmark detection\u0000baselines. The first of their kinds, the dataset with landmark precise timing\u0000information, landmark extraction tool and baselines are designed to support a\u0000wide variety of future research.","PeriodicalId":501284,"journal":{"name":"arXiv - EE - Audio and Speech Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Faster Speech-LLaMA Inference with Multi-token Prediction 利用多标记预测实现更快的语音-LaMA 推断

arXiv - EE - Audio and Speech Processing Pub Date : 2024-09-12 DOI: arxiv-2409.08148

Desh Raj, Gil Keren, Junteng Jia, Jay Mahadeokar, Ozlem Kalinli

引用次数: 0

The Faetar Benchmark: Speech Recognition in a Very Under-Resourced Language Faetar 基准：资源极度匮乏语言的语音识别

arXiv - EE - Audio and Speech Processing Pub Date : 2024-09-12 DOI: arxiv-2409.08103

Michael Ong, Sean Robertson, Leo Peckham, Alba Jorquera Jimenez de Aberasturi, Paula Arkhangorodsky, Robin Huo, Aman Sakhardande, Mark Hallap, Naomi Nagy, Ewan Dunbar

引用次数: 0

Universal Pooling Method of Multi-layer Features from Pretrained Models for Speaker Verification 从预训练模型中提取多层特征的通用汇集法用于扬声器验证

arXiv - EE - Audio and Speech Processing Pub Date : 2024-09-12 DOI: arxiv-2409.07770

Jin Sob Kim, Hyun Joon Park, Wooseok Shin, Sung Won Han

{"title":"Universal Pooling Method of Multi-layer Features from Pretrained Models for Speaker Verification","authors":"Jin Sob Kim, Hyun Joon Park, Wooseok Shin, Sung Won Han","doi":"arxiv-2409.07770","DOIUrl":"https://doi.org/arxiv-2409.07770","url":null,"abstract":"Recent advancements in automatic speaker verification (ASV) studies have been\u0000achieved by leveraging large-scale pretrained networks. In this study, we\u0000analyze the approaches toward such a paradigm and underline the significance of\u0000interlayer information processing as a result. Accordingly, we present a novel\u0000approach for exploiting the multilayered nature of pretrained models for ASV,\u0000which comprises a layer/frame-level network and two steps of pooling\u0000architectures for each layer and frame axis. Specifically, we let convolutional\u0000architecture directly processes a stack of layer outputs.Then, we present a\u0000channel attention-based scheme of gauging layer significance and squeeze the\u0000layer level with the most representative value. Finally, attentive statistics\u0000over frame-level representations yield a single vector speaker embedding.\u0000Comparative experiments are designed using versatile data environments and\u0000diverse pretraining models to validate the proposed approach. The experimental\u0000results demonstrate the stability of the approach using multi-layer outputs in\u0000leveraging pretrained architectures. Then, we verify the superiority of the\u0000proposed ASV backend structure, which involves layer-wise operations, in terms\u0000of performance improvement along with cost efficiency compared to the\u0000conventional method. The ablation study shows how the proposed interlayer\u0000processing aids in maximizing the advantage of utilizing pretrained models.","PeriodicalId":501284,"journal":{"name":"arXiv - EE - Audio and Speech Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Neural Networks for Parkinsons Disease Detection 用于帕金森病检测的图神经网络

arXiv - EE - Audio and Speech Processing Pub Date : 2024-09-12 DOI: arxiv-2409.07884

Shakeel A. Sheikh, Yacouba Kaloga, Ina Kodrasi

{"title":"Graph Neural Networks for Parkinsons Disease Detection","authors":"Shakeel A. Sheikh, Yacouba Kaloga, Ina Kodrasi","doi":"arxiv-2409.07884","DOIUrl":"https://doi.org/arxiv-2409.07884","url":null,"abstract":"Despite the promising performance of state of the art approaches for\u0000Parkinsons Disease (PD) detection, these approaches often analyze individual\u0000speech segments in isolation, which can lead to suboptimal results. Dysarthric\u0000cues that characterize speech impairments from PD patients are expected to be\u0000related across segments from different speakers. Isolated segment analysis\u0000fails to exploit these inter segment relationships. Additionally, not all\u0000speech segments from PD patients exhibit clear dysarthric symptoms, introducing\u0000label noise that can negatively affect the performance and generalizability of\u0000current approaches. To address these challenges, we propose a novel PD\u0000detection framework utilizing Graph Convolutional Networks (GCNs). By\u0000representing speech segments as nodes and capturing the similarity between\u0000segments through edges, our GCN model facilitates the aggregation of dysarthric\u0000cues across the graph, effectively exploiting segment relationships and\u0000mitigating the impact of label noise. Experimental results demonstrate\u0000theadvantages of the proposed GCN model for PD detection and provide insights\u0000into its underlying mechanisms","PeriodicalId":501284,"journal":{"name":"arXiv - EE - Audio and Speech Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Models 通过预训练音频模型的低库适应性微调改进异常声音检测

arXiv - EE - Audio and Speech Processing Pub Date : 2024-09-11 DOI: arxiv-2409.07016

Xinhu Zheng, Anbai Jiang, Bing Han, Yanmin Qian, Pingyi Fan, Jia Liu, Wei-Qiang Zhang

{"title":"Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Models","authors":"Xinhu Zheng, Anbai Jiang, Bing Han, Yanmin Qian, Pingyi Fan, Jia Liu, Wei-Qiang Zhang","doi":"arxiv-2409.07016","DOIUrl":"https://doi.org/arxiv-2409.07016","url":null,"abstract":"Anomalous Sound Detection (ASD) has gained significant interest through the\u0000application of various Artificial Intelligence (AI) technologies in industrial\u0000settings. Though possessing great potential, ASD systems can hardly be readily\u0000deployed in real production sites due to the generalization problem, which is\u0000primarily caused by the difficulty of data collection and the complexity of\u0000environmental factors. This paper introduces a robust ASD model that leverages\u0000audio pre-trained models. Specifically, we fine-tune these models using machine\u0000operation data, employing SpecAug as a data augmentation strategy.\u0000Additionally, we investigate the impact of utilizing Low-Rank Adaptation (LoRA)\u0000tuning instead of full fine-tuning to address the problem of limited data for\u0000fine-tuning. Our experiments on the DCASE2023 Task 2 dataset establish a new\u0000benchmark of 77.75% on the evaluation set, with a significant improvement of\u00006.48% compared with previous state-of-the-art (SOTA) models, including top-tier\u0000traditional convolutional networks and speech pre-trained models, which\u0000demonstrates the effectiveness of audio pre-trained models with LoRA tuning.\u0000Ablation studies are also conducted to showcase the efficacy of the proposed\u0000scheme.","PeriodicalId":501284,"journal":{"name":"arXiv - EE - Audio and Speech Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0