Interspeech最新文献_第10页

ASR-Robust Natural Language Understanding on ASR-GLUE dataset 基于ASR-GLUE数据集的ASR鲁棒自然语言理解

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10097

Lingyun Feng, Jianwei Yu, Yan Wang, Songxiang Liu, Deng Cai, Haitao Zheng

引用次数: 0

A Complementary Joint Training Approach Using Unpaired Speech and Text A Complementary Joint Training Approach Using Unpaired Speech and Text 一种使用非配对语音和文本的互补联合训练方法

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-291

Ye Du, J. Zhang, Qiu-shi Zhu, Lirong Dai, Ming Wu, Xin Fang, Zhouwang Yang

引用次数: 0

Effects of Noise on Speech Perception and Spoken Word Comprehension 噪声对语音感知和口语理解的影响

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10543

Jovan Eranovic, D. Pape, M. Stroińska, E. Service, Marijana Matkovski

引用次数: 0

State & Trait Measurement from Nonverbal Vocalizations: A Multi-Task Joint Learning Approach 非言语语音的状态和特质测量：一种多任务联合学习方法

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10927

Alice Baird, Panagiotis Tzirakis, Jeff Brooks, Lauren Kim, Michael Opara, Christopher B. Gregory, Jacob Metrick, Garrett Boseck, D. Keltner, Alan S. Cowen

{"title":"State & Trait Measurement from Nonverbal Vocalizations: A Multi-Task Joint Learning Approach","authors":"Alice Baird, Panagiotis Tzirakis, Jeff Brooks, Lauren Kim, Michael Opara, Christopher B. Gregory, Jacob Metrick, Garrett Boseck, D. Keltner, Alan S. Cowen","doi":"10.21437/interspeech.2022-10927","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10927","url":null,"abstract":"Humans infer a wide array of meanings from expressive nonverbal vocalizations, e.g., laughs, cries, and sighs. Thus far, computational research has primarily focused on the coarse classification of vocalizations such as laughs, but that approach overlooks significant variations in the meaning of distinct laughs (e.g., amusement, awkwardness, triumph) and the rich array of more nuanced vocalizations people form. Nonverbal vocalizations are shaped by the emotional state an individual chooses to convey, their wellbeing, and (as with the voice more broadly) their identity-related traits. In the present study, we utilize a large-scale dataset comprising more than 35 hours of densely labeled vocal bursts to model emotionally expressive states and demographic traits from nonverbal vocalizations. We compare a single-task and multi-task deep learning architecture to explore how models can leverage acoustic co-dependencies that may exist between the expression of 10 emotions by vocal bursts and the demographic traits of the speaker. Results show that nonverbal vocalizations can be reliably leveraged to predict emotional expression, age, and country of origin. In a multi-task setting, our experiments show that joint learning of emotional expression and demographic traits appears to yield robust results, primarily beneficial for the classification of a speaker’s country of origin.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46758450","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: 1

Analysis of expressivity transfer in non-autoregressive end-to-end multispeaker TTS systems 非自回归端到端多语TTS系统的表达性迁移分析

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10388

Ajinkya Kulkarni, Vincent Colotte, D. Jouvet

{"title":"Analysis of expressivity transfer in non-autoregressive end-to-end multispeaker TTS systems","authors":"Ajinkya Kulkarni, Vincent Colotte, D. Jouvet","doi":"10.21437/interspeech.2022-10388","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10388","url":null,"abstract":"The main objective of this work is to study the expressivity transfer in a speaker’s voice for which no expressive speech data is available in non-autoregressive end-to-end TTS systems. We investigated the expressivity transfer capability of probability density estimation based on deep generative models, namely Generative Flow (Glow) and diffusion probabilistic models (DPM). The usage of deep generative models provides better log likelihood estimates and tractability of the system, subsequently providing high-quality speech synthesis with faster inference speed. Furthermore, we propose the usage of various expressivity encoders, which assist in expressivity transfer in the text-to-speech (TTS) system. More precisely, we used self-attention statistical pooling and multi-scale expressivity encoder architectures for creating a meaningful representation of expressivity. In addition to traditional subjective metrics used for speech synthesis evaluation, we incorporated cosine-similarity to measure the strength of attributes associated with speaker and expressivity. The performance of a non-autoregressive TTS system with a multi-scale expressivity encoder showed better expressivity transfer on Glow and DPM-based decoders. Thus, illustrating the ability of multi-scale architecture to apprehend the underlying attributes of expressivity from multiple acoustic features.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46778202","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: 1

A blueprint for using deepfakes in sociolinguistic matched-guise experiments 在社会语言学匹配伪装实验中使用深度伪造的蓝图

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10782

N. Young, D. Britain, A. Leemann

引用次数: 0

End-to-End Audio-Visual Neural Speaker Diarization 端到端视听神经扬声器日记

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10106

Maokui He, Jun Du, Chin-Hui Lee

{"title":"End-to-End Audio-Visual Neural Speaker Diarization","authors":"Maokui He, Jun Du, Chin-Hui Lee","doi":"10.21437/interspeech.2022-10106","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10106","url":null,"abstract":"In this paper, we propose a novel end-to-end neural-network-based audio-visual speaker diarization method. Unlike most existing audio-visual methods, our audio-visual model takes audio features (e.g., FBANKs), multi-speaker lip regions of in-terest (ROIs), and multi-speaker i-vector embbedings as multi-modal inputs. And a set of binary classiﬁcation output layers produces activities of each speaker. With the ﬁnely designed end-to-end structure, the proposed method can explicitly handle the overlapping speech and distinguish between speech and non-speech accurately with multi-modal information. I-vectors are the key point to solve the alignment problem caused by visual modality error (e.g., occlusions, off-screen speakers or unreliable detection). Besides, our audio-visual model is robust to the absence of visual modality, where the diarization performance degrades signiﬁcantly using the visual-only model. Evaluated on the datasets of the ﬁrst multi-model information based speech processing (MISP) challenge, the proposed method achieved diarization error rates (DERs) of 10.1%/9.5% on development/eval set with reference voice activity detection (VAD) information, while audio-only and video-only system yielded DERs of 27.9%/29.0% and 14.6%/13.1% respectively.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44341659","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: 9

Speaking Rate Control of end-to-end TTS Models by Direct Manipulation of the Encoder's Output Embeddings 直接操纵编码器输出嵌入的端到端TTS模型的说话速率控制

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-759

Martin Lenglet, O. Perrotin, G. Bailly

引用次数: 2

Automatic Detection of Reactive Attachment Disorder Through Turn-Taking Analysis in Clinical Child-Caregiver Sessions 在临床儿童看护会议中通过轮流分析自动检测反应性依恋障碍

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-387

Andréi Birladeanu, H. Minnis, A. Vinciarelli

引用次数: 0

Ant Multilingual Recognition System for OLR 2021 Challenge 面向OLR 2021挑战赛的蚂蚁多语言识别系统

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-355

Anqi Lyu, Zhiming Wang, Huijia Zhu

{"title":"Ant Multilingual Recognition System for OLR 2021 Challenge","authors":"Anqi Lyu, Zhiming Wang, Huijia Zhu","doi":"10.21437/interspeech.2022-355","DOIUrl":"https://doi.org/10.21437/interspeech.2022-355","url":null,"abstract":"This paper presents a comprehensive description of the Ant multilingual recognition system for the 6th Oriental Language Recognition(OLR 2021) Challenge. Inspired by the transfer learning scheme, the encoder components of language iden-tification(LID) model is initialized from pretrained automatic speech recognition(ASR) networks for integrating the lexical phonetic information into language identification. The ASR model is encoder-decoder networks based on U2++ architecture [1]; then inheriting the shared conformer encoder [2] from pretrained ASR model which is effective at global information capturing and local invariance modeling, the LID model, with an attentive statistical pooling layer and a following linear projection layer added on the encoder, is further finetuned until its optimum. Furthermore, data augmentation, score normalization and model ensemble are good strategies to improve performance indicators, which are investigated and analysed in detail within our paper. In the OLR 2021 Challenge, our submitted systems ranked the top in both tasks 1 and 2 with primary met-rics of 0.0025 and 0.0039 respectively, less than 1/3 of the second place 1 , which fully illustrates that our methodologies for multilingual identification are effectual and competitive in real-life scenarios.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45084675","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: 3