Interspeech最新文献_第4页

Perceptual Evaluation of Penetrating Voices through a Semantic Differential Method 语义差分法对穿透性语音的感知评价

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-100

T. Kitamura, Naoki Kunimoto, Hideki Kawahara, S. Amano

引用次数: 0

CALM: Constrastive Cross-modal Speaking Style Modeling for Expressive Text-to-Speech Synthesis 用于表达文本到语音合成的约束跨模态说话风格建模

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-11275

Yi Meng, Xiang Li, Zhiyong Wu, Tingtian Li, Zixun Sun, Xinyu Xiao, Chi Sun, Hui Zhan, H. Meng

引用次数: 4

Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting 基于空间感知的多渠道多方会议发言人日记

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/Interspeech.2022-11412

Jie Wang, Yuji Liu, Binling Wang, Yiming Zhi, Song Li, Shipeng Xia, Jiayang Zhang, Feng Tong, Lin Li, Q. Hong

引用次数: 6

Online Learning of Open-set Speaker Identification by Active User-registration 基于主动用户注册的开放集说话人识别在线学习

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-25

Eunkyung Yoo, H. Song, Taehyeong Kim, Chul Lee

引用次数: 1

MSDWild: Multi-modal Speaker Diarization Dataset in the Wild MSDWild:狂野中的多模态说话人日记数据集

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10466

Tao Liu, Shuai Fan, Xu Xiang, Hongbo Song, Shaoxiong Lin, Jiaqi Sun, Tianyuan Han, Siyuan Chen, Binwei Yao, Sen Liu, Yifei Wu, Y. Qian, Kai Yu

引用次数: 6

Zero-Shot Foreign Accent Conversion without a Native Reference 没有本机引用的零样本外来重音转换

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10664

Waris Quamer, Anurag Das, John M. Levis, E. Chukharev-Hudilainen, R. Gutierrez-Osuna

{"title":"Zero-Shot Foreign Accent Conversion without a Native Reference","authors":"Waris Quamer, Anurag Das, John M. Levis, E. Chukharev-Hudilainen, R. Gutierrez-Osuna","doi":"10.21437/interspeech.2022-10664","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10664","url":null,"abstract":"Previous approaches for foreign accent conversion (FAC) ei-ther need a reference utterance from a native speaker (L1) during synthesis, or are dedicated one-to-one systems that must be trained separately for each non-native (L2) speaker. To address both issues, we propose a new FAC system that can transform L2 speech directly from previously unseen speakers. The system consists of two independent modules: a translator and a synthesizer, which operate on bottleneck features derived from phonetic posteriorgrams. The translator is trained to map bottleneck features in L2 utterances into those from a parallel L1 utterance. The synthesizer is a many-to-many system that maps input bottleneck features into the corresponding Mel-spectrograms, conditioned on an embedding from the L2 speaker. During inference, both modules operate in sequence to take an unseen L2 utterance and generate a native-accented Mel-spectrogram. Perceptual experiments show that our system achieves a large reduction (67%) in non-native accentedness compared to a state-of-the-art reference-free system (28.9%) that builds a dedicated model for each L2 speaker. Moreover, 80% of the listeners rated the synthesized utterances to have the same voice identity as the L2 speaker.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"4920-4924"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43009987","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: 5

Effects of laryngeal manipulations on voice gender perception 喉部操作对声音性别感知的影响

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10815

Zhaoyan Zhang, Jason Zhang, J. Kreiman

{"title":"Effects of laryngeal manipulations on voice gender perception","authors":"Zhaoyan Zhang, Jason Zhang, J. Kreiman","doi":"10.21437/interspeech.2022-10815","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10815","url":null,"abstract":"This study aims to identify laryngeal manipulations that would allow a male to approximate a female-sounding voice, and that can be targeted in voice feminization surgery or therapy. Synthetic voices were generated using a three-dimensional vocal fold model with parametric variations in vocal fold geometry, stiffness, adduction, and subglottal pressure. The vocal tract was kept constant in order to focus on the contribution of laryngeal manipulations. Listening subjects were asked to judge if a voice sounded male or female, or if they were unsure. Results showed the expected large effect of the fundamental frequency (F0) and a moderate effect of spectral shape on gender perception. A mismatch between F0 and spectral shape cues (e.g., low F0 paired with high H1-H2) contributed to ambiguity in gender perception, particularly for voices with F0 in the intermediate range between those of typical adult males and females. Physiologically, the results showed that a female-sounding voice can be produced by decreasing vocal fold thickness and increasing vocal fold transverse stiffness in the coronal plane, changes in which modified both F0 and spectral shape. In contrast, laryngeal manipulations with limited impact on F0 or spectral shape were shown to be less effective in modifying gender perception.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"1856-1860"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43054390","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

Incremental learning for RNN-Transducer based speech recognition models 基于RNN传感器的语音识别模型的增量学习

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-10795

Deepak Baby, Pasquale D’Alterio, Valentin Mendelev

{"title":"Incremental learning for RNN-Transducer based speech recognition models","authors":"Deepak Baby, Pasquale D’Alterio, Valentin Mendelev","doi":"10.21437/interspeech.2022-10795","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10795","url":null,"abstract":"This paper investigates an incremental learning framework for a real-world voice assistant employing RNN-Transducer based automatic speech recognition (ASR) model. Such a model needs to be regularly updated to keep up with changing distribution of customer requests. We demonstrate that a simple ﬁne-tuning approach with a combination of old and new training data can be used to incrementally update the model spending only several hours of training time and without any degradation on old data. This paper explores multiple rounds of incremental updates on the ASR model with monthly training data. Results show that the proposed approach achieves 5-6% relative WER improvement over the models trained from scratch on the monthly evaluation datasets. In addition, we explore if it is pos-sible to improve recognition of speciﬁc new words. We simulate multiple rounds of incremental updates with handful of training utterances per word (both real and synthetic) and show that the recognition of the new words improves dramatically but with a minor degradation on general data. Finally, we demonstrate that the observed degradation on general data can be mitigated by interleaving monthly updates with updates targeting speciﬁc words.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"71-75"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47633462","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: 5

Adversarial-Free Speaker Identity-Invariant Representation Learning for Automatic Dysarthric Speech Classification 用于构音障碍语音自动分类的对抗性自由说话人身份不变表示学习

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-402

Parvaneh Janbakhshi, I. Kodrasi

引用次数: 0

Knowledge distillation for In-memory keyword spotting model 内存关键字识别模型的知识精馏

Interspeech Pub Date : 2022-09-18 DOI: 10.21437/interspeech.2022-633

Zeyang Song, Qi Liu, Qu Yang, Haizhou Li

引用次数: 1