Interspeech最新文献_第5页

Phonetic Analysis of Self-supervised Representations of English Speech 英语语音自监督表征的语音分析

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10884

Dan Wells, Hao Tang, Korin Richmond

引用次数: 8

W2V2-Light: A Lightweight Version of Wav2vec 2.0 for Automatic Speech Recognition W2V2 Light：用于自动语音识别的Wav2vec 2.0的轻量级版本

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10339

Dong-Hyun Kim, Jaehwan Lee, J. Mo, Joon‐Hyuk Chang

引用次数: 4

Autoencoder-Based Tongue Shape Estimation During Continuous Speech 基于自编码器的连续语音舌形估计

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10272

Vinicius Ribeiro, Y. Laprie

引用次数: 2

Unsupervised Acoustic-to-Articulatory Inversion with Variable Vocal Tract Anatomy 无监督声学-发音倒置与可变声道解剖

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-477

Yifan Sun, Qinlong Huang, Xihong Wu

{"title":"Unsupervised Acoustic-to-Articulatory Inversion with Variable Vocal Tract Anatomy","authors":"Yifan Sun, Qinlong Huang, Xihong Wu","doi":"10.21437/interspeech.2022-477","DOIUrl":"https://doi.org/10.21437/interspeech.2022-477","url":null,"abstract":"Acoustic and articulatory variability across speakers has al-ways limited the generalization performance of acoustic-to-articulatory inversion (AAI) methods. Speaker-independent AAI (SI-AAI) methods generally focus on the transformation of acoustic features, but rarely consider the direct matching in the articulatory space. Unsupervised AAI methods have the potential of better generalization ability but typically use a ﬁxed mor-phological setting of a physical articulatory synthesizer even for different speakers, which may cause nonnegligible articulatory compensation. In this paper, we propose to jointly estimate articulatory movements and vocal tract anatomy during the inversion of speech. An unsupervised AAI framework is employed, where estimated vocal tract anatomy is used to set the conﬁguration of a physical articulatory synthesizer, which in turn is driven by estimated articulation movements to imitate a given speech. Experiments show that the estimation of vocal tract anatomy can bring both acoustic and articulatory beneﬁts. Acoustically, the reconstruction quality is higher; articulatorily, the estimated articulatory movement trajectories better match the measured ones. Moreover, the estimated anatomy parameters show clear clusterings by speakers, indicating successful decoupling of speaker characteristics and linguistic content.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"4656-4660"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44404742","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: 2

Combining Simple but Novel Data Augmentation Methods for Improving Conformer ASR 结合简单但新颖的数据增强方法改进保形ASR

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10835

Ronit Damania, Christopher Homan, Emily Tucker Prud'hommeaux

引用次数: 0

Streaming model for Acoustic to Articulatory Inversion with transformer networks 基于变压器网络的声-铰接反演流模型

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10159

Sathvik Udupa, Aravind Illa, P. Ghosh

引用次数: 2

Gram Vaani ASR Challenge on spontaneous telephone speech recordings in regional variations of Hindi Gram Vaani ASR挑战印地语地区变体的自发电话语音记录

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-11371

Anish Bhanushali, Grant Bridgman, Deekshitha G, P. Ghosh, Pratik Kumar, Saurabh Kumar, Adithya Raj Kolladath, Nithya Ravi, Aaditeshwar Seth, Ashish Seth, Abhayjeet Singh, Vrunda N. Sukhadia, Umesh S, Sathvik Udupa, L. D. Prasad

{"title":"Gram Vaani ASR Challenge on spontaneous telephone speech recordings in regional variations of Hindi","authors":"Anish Bhanushali, Grant Bridgman, Deekshitha G, P. Ghosh, Pratik Kumar, Saurabh Kumar, Adithya Raj Kolladath, Nithya Ravi, Aaditeshwar Seth, Ashish Seth, Abhayjeet Singh, Vrunda N. Sukhadia, Umesh S, Sathvik Udupa, L. D. Prasad","doi":"10.21437/interspeech.2022-11371","DOIUrl":"https://doi.org/10.21437/interspeech.2022-11371","url":null,"abstract":"This paper describes the corpus and baseline systems for the Gram Vaani Automatic Speech Recognition (ASR) challenge in regional variations of Hindi. The corpus for this challenge comprises the spontaneous telephone speech recordings collected by a social technology enterprise, Gram Vaani . The regional variations of Hindi together with spontaneity of speech, natural background and transcriptions with variable accuracy due to crowdsourcing make it a unique corpus for ASR on spontaneous telephonic speech. Around, 1108 hours of real-world spontaneous speech recordings, including 1000 hours of unlabelled training data, 100 hours of labelled training data, 5 hours of development data and 3 hours of evaluation data, have been released as a part of the challenge. The efficacy of both training and test sets are validated on different ASR systems in both traditional time-delay neural network-hidden Markov model (TDNN-HMM) frameworks and fully-neural end-to-end (E2E) setup. The word error rate (WER) and character error rate (CER) on eval set for a TDNN model trained on 100 hours of labelled data are 29 . 7% and 15 . 1% , respectively. While, in E2E setup, WER and CER on eval set for a conformer model trained on 100 hours of data are 32 . 9% and 19 . 0% , respectively.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"3548-3552"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43519978","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: 4

Audio-Visual Speech Recognition in MISP2021 Challenge: Dataset Release and Deep Analysis MISP2021挑战中的视听语音识别：数据集发布和深度分析

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10483

Hang Chen, Jun Du, Yusheng Dai, Chin-Hui Lee, S. Siniscalchi, Shinji Watanabe, O. Scharenborg, Jingdong Chen, Baocai Yin, Jia Pan

{"title":"Audio-Visual Speech Recognition in MISP2021 Challenge: Dataset Release and Deep Analysis","authors":"Hang Chen, Jun Du, Yusheng Dai, Chin-Hui Lee, S. Siniscalchi, Shinji Watanabe, O. Scharenborg, Jingdong Chen, Baocai Yin, Jia Pan","doi":"10.21437/interspeech.2022-10483","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10483","url":null,"abstract":"In this paper, we present the updated Audio-Visual Speech Recognition (AVSR) corpus of MISP2021 challenge, a large-scale audio-visual Chinese conversational corpus consisting of 141h audio and video data collected by far/middle/near microphones and far/middle cameras in 34 real-home TV rooms. To our best knowledge, our corpus is the first distant multi-microphone conversational Chinese audio-visual corpus and the first large vocabulary continuous Chinese lip-reading dataset in the adverse home-tv scenario. Moreover, we make a deep analysis of the corpus and conduct a comprehensive ablation study of all audio and video data in the audio-only/video-only/audio-visual systems. Error analysis shows video modality supplement acoustic information degraded by noise to reduce deletion errors and provide discriminative information in overlapping speech to reduce substitution errors. Finally, we also design a set of experiments such as frontend, data augmentation and end-to-end models for providing the direction of potential future work. The corpus 1 and the code 2 are released to promote the research not only in speech area but also for the computer vision area and cross-disciplinary research.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"1766-1770"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43761010","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: 12

Data Augmentation for End-to-end Silent Speech Recognition for Laryngectomees 基于数据增强的Laryntomes端到端无声语音识别

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10868

Beiming Cao, Kristin J. Teplansky, Nordine Sebkhi, Arpan Bhavsar, O. Inan, Robin A. Samlan, T. Mau, Jun Wang

{"title":"Data Augmentation for End-to-end Silent Speech Recognition for Laryngectomees","authors":"Beiming Cao, Kristin J. Teplansky, Nordine Sebkhi, Arpan Bhavsar, O. Inan, Robin A. Samlan, T. Mau, Jun Wang","doi":"10.21437/interspeech.2022-10868","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10868","url":null,"abstract":"Silent speech recognition (SSR) predicts textual information from silent articulation, which is an algorithm design in silent speech interfaces (SSIs). SSIs have the potential of recov-ering the speech ability of individuals who lost their voice but can still articulate (e.g., laryngectomees). Due to the lo-gistic difficulties in articulatory data collection, current SSR studies suffer limited amount of dataset. Data augmentation aims to increase the training data amount by introducing variations into the existing dataset, but has rarely been investigated in SSR for laryngectomees. In this study, we investigated the effectiveness of multiple data augmentation approaches for SSR including consecutive and intermittent time masking, articulatory dimension masking, sinusoidal noise injection and randomly scaling. Different experimental setups including speaker-dependent, speaker-independent, and speaker-adaptive were used. The SSR models were end-to-end speech recognition models trained with connectionist temporal classification (CTC). Electromagnetic articulography (EMA) datasets collected from multiple healthy speakers and laryngectomees were used. The experimental results have demonstrated that the data augmentation approaches explored performed differently, but generally improved SSR performance. Especially, the consecutive time masking has brought significant improvement on SSR for both healthy speakers and laryngectomees.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"3653-3657"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43442432","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: 4

Improving Spoken Language Understanding with Cross-Modal Contrastive Learning 运用跨模态对比学习提高口语理解能力

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-658

Jingjing Dong, Jiayi Fu, P. Zhou, Hao Li, Xiaorui Wang

{"title":"Improving Spoken Language Understanding with Cross-Modal Contrastive Learning","authors":"Jingjing Dong, Jiayi Fu, P. Zhou, Hao Li, Xiaorui Wang","doi":"10.21437/interspeech.2022-658","DOIUrl":"https://doi.org/10.21437/interspeech.2022-658","url":null,"abstract":"Spoken language understanding(SLU) is conventionally based on pipeline architecture with error propagation issues. To mitigate this problem, end-to-end(E2E) models are proposed to directly map speech input to desired semantic outputs. Mean-while, others try to leverage linguistic information in addition to acoustic information by adopting a multi-modal architecture. In this work, we propose a novel multi-modal SLU method, named CMCL, which utilizes cross-modal contrastive learning to learn better multi-modal representation. In particular, a two-stream multi-modal framework is designed, and a contrastive learning task is performed across speech and text representations. More-over, CMCL employs a multi-modal shared classification task combined with a contrastive learning task to guide the learned representation to improve the performance on the intent classification task. We also investigate the efficacy of employing cross-modal contrastive learning during pretraining. CMCL achieves 99.69% and 92.50% accuracy on FSC and Smartlights datasets, respectively, outperforming state-of-the-art comparative methods. Also, performances only decrease by 0.32% and 2.8%, respectively, when trained on 10% and 1% of the FSC dataset, indicating its advantage under few-shot scenarios.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"2693-2697"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43733271","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: 2