Pub Date : 2022-09-18DOI: 10.21437/interspeech.2022-10664
Waris Quamer, Anurag Das, John M. Levis, E. Chukharev-Hudilainen, R. Gutierrez-Osuna
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.
{"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}
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.
{"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}
Pub Date : 2022-09-18DOI: 10.21437/interspeech.2022-402
Parvaneh Janbakhshi, I. Kodrasi
Speech representations which are robust to pathology-unrelated cues such as speaker identity information have been shown to be advantageous for automatic dysarthric speech classification. A recently proposed technique to learn speaker identity-invariant representations for dysarthric speech classification is based on adversarial training. However, adversarial training can be challenging, unstable, and sensitive to training parameters. To avoid adversarial training, in this paper we propose to learn speaker-identity invariant representations exploiting a feature separation framework relying on mutual information minimization. Experimental results on a database of neurotypical and dysarthric speech show that the proposed adversarial-free framework successfully learns speaker identity-invariant representations. Further, it is shown that such representations result in a similar dysarthric speech classification performance as the representations obtained using adversarial training, while the training procedure is more stable and less sensitive to training parameters.
{"title":"Adversarial-Free Speaker Identity-Invariant Representation Learning for Automatic Dysarthric Speech Classification","authors":"Parvaneh Janbakhshi, I. Kodrasi","doi":"10.21437/interspeech.2022-402","DOIUrl":"https://doi.org/10.21437/interspeech.2022-402","url":null,"abstract":"Speech representations which are robust to pathology-unrelated cues such as speaker identity information have been shown to be advantageous for automatic dysarthric speech classification. A recently proposed technique to learn speaker identity-invariant representations for dysarthric speech classification is based on adversarial training. However, adversarial training can be challenging, unstable, and sensitive to training parameters. To avoid adversarial training, in this paper we propose to learn speaker-identity invariant representations exploiting a feature separation framework relying on mutual information minimization. Experimental results on a database of neurotypical and dysarthric speech show that the proposed adversarial-free framework successfully learns speaker identity-invariant representations. Further, it is shown that such representations result in a similar dysarthric speech classification performance as the representations obtained using adversarial training, while the training procedure is more stable and less sensitive to training parameters.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"2138-2142"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48272141","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}
Pub Date : 2022-09-18DOI: 10.21437/interspeech.2022-633
Zeyang Song, Qi Liu, Qu Yang, Haizhou Li
We study a light-weight implementation of keyword spotting (KWS) for voice command and control, that can be implemented on an in-memory computing (IMC) unit with same accuracy at a lower computational cost than the state-of-the-art methods. KWS is expected to be always-on for mobile devices with limited resources. IMC represents one of the solutions. However, it only supports multiplication-accumulation and Boolean operations. We note that common feature extraction methods, such as MFCC and SincConv, are not supported by IMC as they depend on expensive logarithm computing. On the other hand, some neural network solutions to KWS involve a large number of parameters that are not feasible for mobile devices. In this work, we propose a knowledge distillation technique to replace the complex speech frontend like MFCC or SincConv with a light-weight encoder without performance loss. Experiments show that the proposed model outperforms the KWS model with MFCC and SincConv front-end in terms of accuracy and computational cost.
{"title":"Knowledge distillation for In-memory keyword spotting model","authors":"Zeyang Song, Qi Liu, Qu Yang, Haizhou Li","doi":"10.21437/interspeech.2022-633","DOIUrl":"https://doi.org/10.21437/interspeech.2022-633","url":null,"abstract":"We study a light-weight implementation of keyword spotting (KWS) for voice command and control, that can be implemented on an in-memory computing (IMC) unit with same accuracy at a lower computational cost than the state-of-the-art methods. KWS is expected to be always-on for mobile devices with limited resources. IMC represents one of the solutions. However, it only supports multiplication-accumulation and Boolean operations. We note that common feature extraction methods, such as MFCC and SincConv, are not supported by IMC as they depend on expensive logarithm computing. On the other hand, some neural network solutions to KWS involve a large number of parameters that are not feasible for mobile devices. In this work, we propose a knowledge distillation technique to replace the complex speech frontend like MFCC or SincConv with a light-weight encoder without performance loss. Experiments show that the proposed model outperforms the KWS model with MFCC and SincConv front-end in terms of accuracy and computational cost.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"4128-4132"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48292021","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}
Pub Date : 2022-09-18DOI: 10.21437/interspeech.2022-865
Min-Kyung Kim, Joon‐Hyuk Chang
This study presents a method for improving the performance of the text-to-speech (TTS) model by using three global speech-style representations: language, speaker, and prosody. Synthesizing different languages and prosody in the speaker’s voice regardless of their own language and prosody is possi-ble. To construct the embedding of each representation conditioned in the TTS model such that it is independent of the other representations, we propose an adversarial training method for the general architecture of TTS models. Furthermore, we introduce a sequential training method that includes rehearsal-based continual learning to train complex and small amounts of data without forgetting previously learned information. The experimental results show that the proposed method can generate good-quality speech and yield high similarity for speakers and prosody, even for representations that the speaker in the dataset does not contain.
{"title":"Adversarial and Sequential Training for Cross-lingual Prosody Transfer TTS","authors":"Min-Kyung Kim, Joon‐Hyuk Chang","doi":"10.21437/interspeech.2022-865","DOIUrl":"https://doi.org/10.21437/interspeech.2022-865","url":null,"abstract":"This study presents a method for improving the performance of the text-to-speech (TTS) model by using three global speech-style representations: language, speaker, and prosody. Synthesizing different languages and prosody in the speaker’s voice regardless of their own language and prosody is possi-ble. To construct the embedding of each representation conditioned in the TTS model such that it is independent of the other representations, we propose an adversarial training method for the general architecture of TTS models. Furthermore, we introduce a sequential training method that includes rehearsal-based continual learning to train complex and small amounts of data without forgetting previously learned information. The experimental results show that the proposed method can generate good-quality speech and yield high similarity for speakers and prosody, even for representations that the speaker in the dataset does not contain.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"4556-4560"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46991331","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}
Pub Date : 2022-09-18DOI: 10.21437/interspeech.2022-10884
Dan Wells, Hao Tang, Korin Richmond
We present an analysis of discrete units discovered via self-supervised representation learning on English speech. We focus on units produced by a pre-trained HuBERT model due to its wide adoption in ASR, speech synthesis, and many other tasks. Whereas previous work has evaluated the quality of such quantization models in aggregate over all phones for a given language, we break our analysis down into broad phonetic classes, taking into account specific aspects of their articulation when consid-ering their alignment to discrete units. We find that these units correspond to sub-phonetic events, and that fine dynamics such as the distinct closure and release portions of plosives tend to be represented by sequences of discrete units. Our work provides a reference for the phonetic properties of discrete units discovered by HuBERT, facilitating analyses of many speech applications based on this model.
{"title":"Phonetic Analysis of Self-supervised Representations of English Speech","authors":"Dan Wells, Hao Tang, Korin Richmond","doi":"10.21437/interspeech.2022-10884","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10884","url":null,"abstract":"We present an analysis of discrete units discovered via self-supervised representation learning on English speech. We focus on units produced by a pre-trained HuBERT model due to its wide adoption in ASR, speech synthesis, and many other tasks. Whereas previous work has evaluated the quality of such quantization models in aggregate over all phones for a given language, we break our analysis down into broad phonetic classes, taking into account specific aspects of their articulation when consid-ering their alignment to discrete units. We find that these units correspond to sub-phonetic events, and that fine dynamics such as the distinct closure and release portions of plosives tend to be represented by sequences of discrete units. Our work provides a reference for the phonetic properties of discrete units discovered by HuBERT, facilitating analyses of many speech applications based on this model.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"3583-3587"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47141667","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}
Pub Date : 2022-09-18DOI: 10.21437/interspeech.2022-10339
Dong-Hyun Kim, Jaehwan Lee, J. Mo, Joon‐Hyuk Chang
Wav2vec 2.0 (W2V2) has shown remarkable speech recognition performance by pre-training only with unlabeled data and fine-tuning with a small amount of labeled data. However, the practical application of W2V2 is hindered by hardware memory limitations, as it contains 317 million parameters. To ad-dress this issue, we propose W2V2-Light, a lightweight version of W2V2. We introduce two simple sharing methods to reduce the memory consumption as well as the computational costs of W2V2. Compared to W2V2, our model has 91% lesser parameters and a speedup of 1.31 times with minor degradation in downstream task performance. Moreover, by quantifying the stability of representations, we provide an empirical insight into why our model is capable of maintaining competitive performance despite the significant reduction in memory
{"title":"W2V2-Light: A Lightweight Version of Wav2vec 2.0 for Automatic Speech Recognition","authors":"Dong-Hyun Kim, Jaehwan Lee, J. Mo, Joon‐Hyuk Chang","doi":"10.21437/interspeech.2022-10339","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10339","url":null,"abstract":"Wav2vec 2.0 (W2V2) has shown remarkable speech recognition performance by pre-training only with unlabeled data and fine-tuning with a small amount of labeled data. However, the practical application of W2V2 is hindered by hardware memory limitations, as it contains 317 million parameters. To ad-dress this issue, we propose W2V2-Light, a lightweight version of W2V2. We introduce two simple sharing methods to reduce the memory consumption as well as the computational costs of W2V2. Compared to W2V2, our model has 91% lesser parameters and a speedup of 1.31 times with minor degradation in downstream task performance. Moreover, by quantifying the stability of representations, we provide an empirical insight into why our model is capable of maintaining competitive performance despite the significant reduction in memory","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"3038-3042"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47360779","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}
Pub Date : 2022-09-18DOI: 10.21437/interspeech.2022-10272
Vinicius Ribeiro, Y. Laprie
Vocal tract shape estimation is a necessary step for articulatory speech synthesis. However, the literature on the topic is scarce, and most current methods lack adequacy to many physical constraints related to speech production. This study proposes an alternative approach to the task to solve specific issues faced in the previous work, especially those related to critical ar-ticulators. We present an autoencoder-based method for tongue shape estimation during continuous speech. An autoencoder is trained to learn the data’s encoding and serves as an auxiliary network for the principal one, which maps phonemes to the shapes. Instead of predicting the exact points in the target curve, the neural network learns how to predict the curve’s main components, i.e., the autoencoder’s representation. We show how this approach allows imposing critical articulators’ constraints, controlling the tongue shape through the latent space, and generating a smooth output without relying on any postprocessing method.
{"title":"Autoencoder-Based Tongue Shape Estimation During Continuous Speech","authors":"Vinicius Ribeiro, Y. Laprie","doi":"10.21437/interspeech.2022-10272","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10272","url":null,"abstract":"Vocal tract shape estimation is a necessary step for articulatory speech synthesis. However, the literature on the topic is scarce, and most current methods lack adequacy to many physical constraints related to speech production. This study proposes an alternative approach to the task to solve specific issues faced in the previous work, especially those related to critical ar-ticulators. We present an autoencoder-based method for tongue shape estimation during continuous speech. An autoencoder is trained to learn the data’s encoding and serves as an auxiliary network for the principal one, which maps phonemes to the shapes. Instead of predicting the exact points in the target curve, the neural network learns how to predict the curve’s main components, i.e., the autoencoder’s representation. We show how this approach allows imposing critical articulators’ constraints, controlling the tongue shape through the latent space, and generating a smooth output without relying on any postprocessing method.","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"86-90"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44213806","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}
Pub Date : 2022-09-18DOI: 10.21437/interspeech.2022-477
Yifan Sun, Qinlong Huang, Xihong Wu
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.
{"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}
Pub Date : 2022-09-18DOI: 10.21437/interspeech.2022-10835
Ronit Damania, Christopher Homan, Emily Tucker Prud'hommeaux
{"title":"Combining Simple but Novel Data Augmentation Methods for Improving Conformer ASR","authors":"Ronit Damania, Christopher Homan, Emily Tucker Prud'hommeaux","doi":"10.21437/interspeech.2022-10835","DOIUrl":"https://doi.org/10.21437/interspeech.2022-10835","url":null,"abstract":"","PeriodicalId":73500,"journal":{"name":"Interspeech","volume":"1 1","pages":"4890-4894"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44483829","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}
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