Interspeech
Collection
Accepted papers for Interspeech (Annual Conference of the International Speech Communication Association), one dataset per year. • 13 items • Updated
paper_id stringlengths 15 35 | title stringlengths 22 190 | authors listlengths 1 20 | isca_url stringlengths 66 86 | pdf_url stringlengths 65 85 | doi stringlengths 28 30 | pages stringlengths 3 9 | bibtex large_stringlengths 286 717 | abstract large_stringlengths 455 1.71k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|
wang23m_interspeech | Emotional Talking Head Generation based on Memory-Sharing and Attention-Augmented Networks | [
"Jianrong Wang",
"Yaxin Zhao",
"Li Liu",
"Tianyi Xu",
"Qi Li",
"Sen Li"
] | https://www.isca-archive.org/interspeech_2023/wang23m_interspeech.html | https://www.isca-archive.org/interspeech_2023/wang23m_interspeech.pdf | 10.21437/Interspeech.2023-749 | 2-6 | @inproceedings{wang23m_interspeech,
title = {{Emotional Talking Head Generation based on Memory-Sharing and Attention-Augmented Networks}},
author = {Jianrong Wang and Yaxin Zhao and Li Liu and Tianyi Xu and Qi Li and Sen Li},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {2--6},
... | Given an audio clip and a reference face image, the goal of the talking head generation is to generate a high-fidelity talking head video. Although some audio-driven methods of generating talking head videos have made some achievements in the past, most of them only focused on lip and audio synchronization and lack the... | 2306.03594 | title_snapshot |
liu23n_interspeech | Speech Synthesis with Self-Supervisedly Learnt Prosodic Representations | [
"Zhao-Ci Liu",
"Zhen-Hua Ling",
"Ya-Jun Hu",
"Jia Pan",
"Jin-Wei Wang",
"Yun-Di Wu"
] | https://www.isca-archive.org/interspeech_2023/liu23n_interspeech.html | https://www.isca-archive.org/interspeech_2023/liu23n_interspeech.pdf | 10.21437/Interspeech.2023-1292 | 7-11 | @inproceedings{liu23n_interspeech,
title = {{Speech Synthesis with Self-Supervisedly Learnt Prosodic Representations}},
author = {Zhao-Ci Liu and Zhen-Hua Ling and Ya-Jun Hu and Jia Pan and Jin-Wei Wang and Yun-Di Wu},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {7--11},
doi ... | This paper presents S4LPR, a Speech Synthesis model conditioned on Self-Supervisedly Learnt Prosodic Representations. Instead of using raw acoustic features, such as F0 and energy, as intermediate prosodic variables, three self-supervised speech models are designed for comparison and are pre-trained on large-scale unla... | null | null |
tang23_interspeech | EmoMix: Emotion Mixing via Diffusion Models for Emotional Speech Synthesis | [
"Haobin Tang",
"Xulong Zhang",
"Jianzong Wang",
"Ning Cheng",
"Jing Xiao"
] | https://www.isca-archive.org/interspeech_2023/tang23_interspeech.html | https://www.isca-archive.org/interspeech_2023/tang23_interspeech.pdf | 10.21437/Interspeech.2023-1317 | 12-16 | @inproceedings{tang23_interspeech,
title = {{EmoMix: Emotion Mixing via Diffusion Models for Emotional Speech Synthesis}},
author = {Haobin Tang and Xulong Zhang and Jianzong Wang and Ning Cheng and Jing Xiao},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {12--16},
doi = {1... | There has been significant progress in emotional Text-To-Speech (TTS) synthesis technology in recent years. However, existing methods primarily focus on the synthesis of a limited number of emotion types and have achieved unsatisfactory performance in intensity control. To address these limitations, we propose EmoMix, ... | 2306.00648 | title_snapshot |
xin23b_interspeech | Laughter Synthesis using Pseudo Phonetic Tokens with a Large-scale In-the-wild Laughter Corpus | [
"Detai Xin",
"Shinnosuke Takamichi",
"Ai Morimatsu",
"Hiroshi Saruwatari"
] | https://www.isca-archive.org/interspeech_2023/xin23b_interspeech.html | https://www.isca-archive.org/interspeech_2023/xin23b_interspeech.pdf | 10.21437/Interspeech.2023-806 | 17-21 | @inproceedings{xin23b_interspeech,
title = {{Laughter Synthesis using Pseudo Phonetic Tokens with a Large-scale In-the-wild Laughter Corpus}},
author = {Detai Xin and Shinnosuke Takamichi and Ai Morimatsu and Hiroshi Saruwatari},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {17--21... | We present a large-scale in-the-wild Japanese laughter corpus and a laughter synthesis method. Previous work on laughter synthesis lacks not only data but also proper ways to represent laughter. To solve these problems, we first propose an in-the-wild corpus comprising 3.5 hours of laughter, which is to our best knowle... | 2305.12442 | title_snapshot |
liu23u_interspeech | Explicit Intensity Control for Accented Text-to-speech | [
"Rui Liu",
"Haolin Zuo",
"De Hu",
"Guanglai Gao",
"Haizhou Li"
] | https://www.isca-archive.org/interspeech_2023/liu23u_interspeech.html | https://www.isca-archive.org/interspeech_2023/liu23u_interspeech.pdf | 10.21437/Interspeech.2023-2270 | 22-26 | @inproceedings{liu23u_interspeech,
title = {{Explicit Intensity Control for Accented Text-to-speech}},
author = {Rui Liu and Haolin Zuo and De Hu and Guanglai Gao and Haizhou Li},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {22--26},
doi = {10.21437/Interspeech.2023-2270},... | Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1). How to control the intensity of accent is a very interesting research direction. Recent works design a speaker-adversarial loss to disentangle the speaker and accent information, and then adju... | 2210.15364 | title_snapshot |
zhang23o_interspeech | Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech | [
"Guangyan Zhang",
"Thomas Merritt",
"Sam Ribeiro",
"Biel Tura-Vecino",
"Kayoko Yanagisawa",
"Kamil Pokora",
"Abdelhamid Ezzerg",
"Sebastian Cygert",
"Ammar Abbas",
"Piotr Bilinski",
"Roberto Barra-Chicote",
"Daniel Korzekwa",
"Jaime Lorenzo-Trueba"
] | https://www.isca-archive.org/interspeech_2023/zhang23o_interspeech.html | https://www.isca-archive.org/interspeech_2023/zhang23o_interspeech.pdf | 10.21437/Interspeech.2023-834 | 27-31 | @inproceedings{zhang23o_interspeech,
title = {{Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech}},
author = {Guangyan Zhang and Thomas Merritt and Sam Ribeiro and Biel Tura-Vecino and Kayoko Yanagisawa and Kamil Pokora and Abdelhamid Ezzerg and Sebastian C... | Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space. Aiming to improve those assumptions, Normalizing Flows and Diffusion Probabilistic Models were recently proposed as alternatives. In this paper, we compare traditional L1/L2... | 2307.16679 | title_snapshot |
duquenne23_interspeech | Modular Speech-to-Text Translation for Zero-Shot Cross-Modal Transfer | [
"Paul-Ambroise Duquenne",
"Holger Schwenk",
"Benoît Sagot"
] | https://www.isca-archive.org/interspeech_2023/duquenne23_interspeech.html | https://www.isca-archive.org/interspeech_2023/duquenne23_interspeech.pdf | 10.21437/Interspeech.2023-2484 | 32-36 | @inproceedings{duquenne23_interspeech,
title = {{Modular Speech-to-Text Translation for Zero-Shot Cross-Modal Transfer}},
author = {Paul-Ambroise Duquenne and Holger Schwenk and Benoît Sagot},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {32--36},
doi = {10.21437/Interspee... | Recent research has shown that independently trained encoders and decoders, combined through a shared fixed-size representation, can achieve competitive performance in speech-to-text translation. In this work, we show that this type of approach can be further improved with multilingual training. We observe significant ... | 2310.03724 | title_snapshot |
pal23_interspeech | Improving Isochronous Machine Translation with Target Factors and Auxiliary Counters | [
"Proyag Pal",
"Brian Thompson",
"Yogesh Virkar",
"Prashant Mathur",
"Alexandra Chronopoulou",
"Marcello Federico"
] | https://www.isca-archive.org/interspeech_2023/pal23_interspeech.html | https://www.isca-archive.org/interspeech_2023/pal23_interspeech.pdf | 10.21437/Interspeech.2023-1063 | 37-41 | @inproceedings{pal23_interspeech,
title = {{Improving Isochronous Machine Translation with Target Factors and Auxiliary Counters}},
author = {Proyag Pal and Brian Thompson and Yogesh Virkar and Prashant Mathur and Alexandra Chronopoulou and Marcello Federico},
year = {2023},
booktitle = {{Interspeec... | To translate speech for automatic dubbing, machine translation needs to be isochronous, i.e. translated speech needs to be aligned with the source in terms of speech durations. We introduce target factors in a transformer model to predict durations jointly with target language phoneme sequences. We also introduce auxil... | 2305.13204 | title_snapshot |
song23_interspeech | StyleS2ST: Zero-shot Style Transfer for Direct Speech-to-speech Translation | [
"Kun Song",
"Yi Ren",
"Yi Lei",
"Chunfeng Wang",
"Kun Wei",
"Lei Xie",
"Xiang Yin",
"Zejun Ma"
] | https://www.isca-archive.org/interspeech_2023/song23_interspeech.html | https://www.isca-archive.org/interspeech_2023/song23_interspeech.pdf | 10.21437/Interspeech.2023-648 | 42-46 | @inproceedings{song23_interspeech,
title = {{StyleS2ST: Zero-shot Style Transfer for Direct Speech-to-speech Translation}},
author = {Kun Song and Yi Ren and Yi Lei and Chunfeng Wang and Kun Wei and Lei Xie and Xiang Yin and Zejun Ma},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {... | Direct speech-to-speech translation (S2ST) has gradually become popular as it has many advantages compared with cascade S2ST. However, current research mainly focuses on the accuracy of semantic translation and ignores the speech style transfer from a source language to a target language. The lack of high-fidelity expr... | 2305.17732 | title_snapshot |
gaido23_interspeech | Joint Speech Translation and Named Entity Recognition | [
"Marco Gaido",
"Sara Papi",
"Matteo Negri",
"Marco Turchi"
] | https://www.isca-archive.org/interspeech_2023/gaido23_interspeech.html | https://www.isca-archive.org/interspeech_2023/gaido23_interspeech.pdf | 10.21437/Interspeech.2023-1767 | 47-51 | @inproceedings{gaido23_interspeech,
title = {{Joint Speech Translation and Named Entity Recognition}},
author = {Marco Gaido and Sara Papi and Matteo Negri and Marco Turchi},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {47--51},
doi = {10.21437/Interspeech.2023-1767},
is... | Modern automatic translation systems aim at supporting the users by providing contextual knowledge. In this framework, a critical task is the output enrichment with information regarding the mentioned entities. This is currently achieved by processing the generated translations with named entity recognition (NER) tools... | 2210.11987 | title_snapshot |
sant23_interspeech | Analysis of Acoustic information in End-to-End Spoken Language Translation | [
"Gerard Sant",
"Carlos Escolano"
] | https://www.isca-archive.org/interspeech_2023/sant23_interspeech.html | https://www.isca-archive.org/interspeech_2023/sant23_interspeech.pdf | 10.21437/Interspeech.2023-2050 | 52-56 | @inproceedings{sant23_interspeech,
title = {{Analysis of Acoustic information in End-to-End Spoken Language Translation}},
author = {Gerard Sant and Carlos Escolano},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {52--56},
doi = {10.21437/Interspeech.2023-2050},
issn ... | End-to-End Transformer-based models are the most popular approach for Spoken Language Translation (SLT). While obtaining state-of-the-art results, we are still far from understanding how these models extract acoustic information from the data and how they are transformed into semantic representations. In this paper, we... | null | null |
wang23oa_interspeech | LAMASSU: A Streaming Language-Agnostic Multilingual Speech Recognition and Translation Model Using Neural Transducers | [
"Peidong Wang",
"Eric Sun",
"Jian Xue",
"Yu Wu",
"Long Zhou",
"Yashesh Gaur",
"Shujie Liu",
"Jinyu Li"
] | https://www.isca-archive.org/interspeech_2023/wang23oa_interspeech.html | https://www.isca-archive.org/interspeech_2023/wang23oa_interspeech.pdf | 10.21437/Interspeech.2023-2004 | 57-61 | @inproceedings{wang23oa_interspeech,
title = {{LAMASSU: A Streaming Language-Agnostic Multilingual Speech Recognition and Translation Model Using Neural Transducers}},
author = {Peidong Wang and Eric Sun and Jian Xue and Yu Wu and Long Zhou and Yashesh Gaur and Shujie Liu and Jinyu Li},
year = {2023},... | Automatic speech recognition (ASR) and speech translation (ST) can both use neural transducers as the model structure. It is thus possible to use a single transducer model to perform both tasks. In real-world applications, such joint ASR and ST models may need to be streaming and do not require source language identifi... | 2211.02809 | title_judge |
peng23c_interspeech | DPHuBERT: Joint Distillation and Pruning of Self-Supervised Speech Models | [
"Yifan Peng",
"Yui Sudo",
"Shakeel Muhammad",
"Shinji Watanabe"
] | https://www.isca-archive.org/interspeech_2023/peng23c_interspeech.html | https://www.isca-archive.org/interspeech_2023/peng23c_interspeech.pdf | 10.21437/Interspeech.2023-1213 | 62-66 | @inproceedings{peng23c_interspeech,
title = {{DPHuBERT: Joint Distillation and Pruning of Self-Supervised Speech Models}},
author = {Yifan Peng and Yui Sudo and Shakeel Muhammad and Shinji Watanabe},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {62--66},
doi = {10.21437/Int... | Self-supervised learning (SSL) has achieved notable success in many speech processing tasks, but the large model size and heavy computational cost hinder the deployment. Knowledge distillation trains a small student model to mimic the behavior of a large teacher model. However, the student architecture usually needs to... | 2305.17651 | title_snapshot |
zaiem23_interspeech | Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations | [
"Salah Zaiem",
"Titouan Parcollet",
"Slim Essid"
] | https://www.isca-archive.org/interspeech_2023/zaiem23_interspeech.html | https://www.isca-archive.org/interspeech_2023/zaiem23_interspeech.pdf | 10.21437/Interspeech.2023-1040 | 67-71 | @inproceedings{zaiem23_interspeech,
title = {{Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations}},
author = {Salah Zaiem and Titouan Parcollet and Slim Essid},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {67--71},
doi = {1... | Self-Supervised Learning (SSL) has allowed leveraging large amounts of unlabeled speech data to improve the performance of speech recognition models even with small annotated datasets. Despite this, speech SSL representations may fail while facing an acoustic mismatch between the pretraining and target datasets. To add... | 2306.00481 | title_snapshot |
yang23e_interspeech | Dual Acoustic Linguistic Self-supervised Representation Learning for Cross-Domain Speech Recognition | [
"Zhao Yang",
"Dianwen Ng",
"Chong Zhang",
"Xiao Fu",
"Rui Jiang",
"Wei Xi",
"Yukun Ma",
"Chongjia Ni",
"Eng Siong Chng",
"Bin Ma",
"Jizhong Zhao"
] | https://www.isca-archive.org/interspeech_2023/yang23e_interspeech.html | https://www.isca-archive.org/interspeech_2023/yang23e_interspeech.pdf | 10.21437/Interspeech.2023-387 | 72-76 | @inproceedings{yang23e_interspeech,
title = {{Dual Acoustic Linguistic Self-supervised Representation Learning for Cross-Domain Speech Recognition}},
author = {Zhao Yang and Dianwen Ng and Chong Zhang and Xiao Fu and Rui Jiang and Wei Xi and Yukun Ma and Chongjia Ni and Eng Siong Chng and Bin Ma and Jizhong ... | The integration of well-pre-trained acoustic and linguistic representations boosts the performance of speech-to-text cross-modality tasks. However, the potential of fine-tuning cross-modality integrated model on accented and noisy corpus is still under-explored. To address this gap, we propose an end-to-end acoustic an... | null | null |
baskar23_interspeech | O-1: Self-training with Oracle and 1-best Hypothesis | [
"Murali Karthick Baskar",
"Andrew Rosenberg",
"Bhuvana Ramabhadran",
"Kartik Audhkhasi"
] | https://www.isca-archive.org/interspeech_2023/baskar23_interspeech.html | https://www.isca-archive.org/interspeech_2023/baskar23_interspeech.pdf | 10.21437/Interspeech.2023-2166 | 77-81 | @inproceedings{baskar23_interspeech,
title = {{O-1: Self-training with Oracle and 1-best Hypothesis}},
author = {Murali Karthick Baskar and Andrew Rosenberg and Bhuvana Ramabhadran and Kartik Audhkhasi},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {77--81},
doi = {10.21437... | We introduce O-1, a new self-training objective to reduce training bias and unify training and evaluation metrics for speech recognition. O-1 is a faster variant of Expected Minimum Bayes Risk (EMBR), that boosts the oracle hypothesis and can accommodate both supervised and unsupervised data. We demonstrate the effecti... | 2308.07486 | title_snapshot |
ma23d_interspeech | MT4SSL: Boosting Self-Supervised Speech Representation Learning by Integrating Multiple Targets | [
"Ziyang Ma",
"Zhisheng Zheng",
"Changli Tang",
"Yujin Wang",
"Xie Chen"
] | https://www.isca-archive.org/interspeech_2023/ma23d_interspeech.html | https://www.isca-archive.org/interspeech_2023/ma23d_interspeech.pdf | 10.21437/Interspeech.2023-822 | 82-86 | @inproceedings{ma23d_interspeech,
title = {{MT4SSL: Boosting Self-Supervised Speech Representation Learning by Integrating Multiple Targets}},
author = {Ziyang Ma and Zhisheng Zheng and Changli Tang and Yujin Wang and Xie Chen},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {82--86}... | In this paper, we provide a new perspective on self-supervised speech models from how the training targets are obtained. We generalize the targets extractor into Offline Targets Extractor (Off-TE) and Online Targets Extractor (On-TE). Based on this, we propose a new multi-tasking learning framework for self-supervised ... | 2211.07321 | title_snapshot |
lamyeemui23_interspeech | Comparing Self-Supervised Pre-Training and Semi-Supervised Training for Speech Recognition in Languages with Weak Language Models | [
"Léa-Marie Lam-Yee-Mui",
"Lucas Ondel Yang",
"OndÅej Klejch"
] | https://www.isca-archive.org/interspeech_2023/lamyeemui23_interspeech.html | https://www.isca-archive.org/interspeech_2023/lamyeemui23_interspeech.pdf | 10.21437/Interspeech.2023-1802 | 87-91 | @inproceedings{lamyeemui23_interspeech,
title = {{Comparing Self-Supervised Pre-Training and Semi-Supervised Training for Speech Recognition in Languages with Weak Language Models}},
author = {Léa-Marie Lam-Yee-Mui and Lucas Ondel Yang and OndÅej Klejch},
year = {2023},
booktitle = {{Interspeech 2... | This paper investigates the potential of improving a hybrid automatic speech recognition model trained on 10 hours of transcribed data with 200 hours of untranscribed data in low-resource languages. First, we compare baseline methods of cross-lingual transfer with MFCC features and features extracted with the multiling... | null | null |
zhang23da_interspeech | Chinese EFL Learnersâ Perception of English Prosodic Focus | [
"Xinya Zhang",
"Ying Chen"
] | https://www.isca-archive.org/interspeech_2023/zhang23da_interspeech.html | https://www.isca-archive.org/interspeech_2023/zhang23da_interspeech.pdf | 10.21437/Interspeech.2023-1781 | 92-96 | @inproceedings{zhang23da_interspeech,
title = {{Chinese EFL Learnersâ Perception of English Prosodic Focus}},
author = {Xinya Zhang and Ying Chen},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {92--96},
doi = {10.21437/Interspeech.2023-1781},
issn = {2958-1796},
} | Focus in a sentence can be realized prosodically in speech communication. It has been found not easy for L2 learners to acquire. The present study examines Chinese learners' perception of English prosodic focus, specifically the effects of learners' English proficiency, intonation type, sentence length, and focus locat... | null | null |
sostarics23_interspeech | Pitch Accent Variation and the Interpretation of Rising and Falling Intonation in American English | [
"Thomas Sostarics",
"Jennifer Cole"
] | https://www.isca-archive.org/interspeech_2023/sostarics23_interspeech.html | https://www.isca-archive.org/interspeech_2023/sostarics23_interspeech.pdf | 10.21437/Interspeech.2023-315 | 97-101 | @inproceedings{sostarics23_interspeech,
title = {{Pitch Accent Variation and the Interpretation of Rising and Falling Intonation in American English}},
author = {Thomas Sostarics and Jennifer Cole},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {97--101},
doi = {10.21437/Int... | This study tests the division of labor in the meaning conveyed by pitch accents and edge tones in English intonation. In three perception studies, we investigate where the locus of the contrast between an assertive vs inquisitive interpretation resides. By doing so, we also gain insight into the role of potentially mea... | null | null |
kuang23_interspeech | Tonal coarticulation as a cue for upcoming prosodic boundary | [
"Jianjing Kuang",
"May Pik Yu Chan",
"Nari Rhee"
] | https://www.isca-archive.org/interspeech_2023/kuang23_interspeech.html | https://www.isca-archive.org/interspeech_2023/kuang23_interspeech.pdf | 10.21437/Interspeech.2023-1033 | 102-106 | @inproceedings{kuang23_interspeech,
title = {{Tonal coarticulation as a cue for upcoming prosodic boundary}},
author = {Jianjing Kuang and May Pik Yu Chan and Nari Rhee},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {102--106},
doi = {10.21437/Interspeech.2023-1033},
issn... | It has been established that the lack of tonal coarticulation or pitch reset is a salient cue for the beginning of a large prosodic domain, however, it is yet unclear whether tonal coarticulation can be an informative cue for the end of a prosodic domain. We examined this question with two continuous speech corpora of ... | null | null |
repp23_interspeech | Alignment of Beat Gestures and Prosodic Prominence in German | [
"Sophie Repp",
"Lara Muhtz",
"Johannes Heim"
] | https://www.isca-archive.org/interspeech_2023/repp23_interspeech.html | https://www.isca-archive.org/interspeech_2023/repp23_interspeech.pdf | 10.21437/Interspeech.2023-2116 | 107-111 | @inproceedings{repp23_interspeech,
title = {{Alignment of Beat Gestures and Prosodic Prominence in German}},
author = {Sophie Repp and Lara Muhtz and Johannes Heim},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {107--111},
doi = {10.21437/Interspeech.2023-2116},
issn ... | We present evidence on the alignment of beat gestures and prosodic prominence from a video corpus consisting of six German educational videos for students from six presenters. Our analysis of 120 beat gestures (with a substantial variety of hand shapes) shows that beat gestures almost always align with prosodically pro... | null | null |
white23_interspeech | Creak Prevalence and Prosodic Context in Australian English | [
"Hannah White",
"Joshua Penney",
"Andy Gibson",
"Anita Szakay",
"Felicity Cox"
] | https://www.isca-archive.org/interspeech_2023/white23_interspeech.html | https://www.isca-archive.org/interspeech_2023/white23_interspeech.pdf | 10.21437/Interspeech.2023-1454 | 112-116 | @inproceedings{white23_interspeech,
title = {{Creak Prevalence and Prosodic Context in Australian English}},
author = {Hannah White and Joshua Penney and Andy Gibson and Anita Szakay and Felicity Cox},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {112--116},
doi = {10.21437... | Creaky voice has been found to mark phrase-finality in many varieties of English, as well as in other languages. The present study aims to investigate whether this is also true for Australian English (AusE), a variety that is understudied in creaky voice research. Using automatic creak detection methods, the need for m... | null | null |
bodur23_interspeech | Speech reduction: position within French prosodic structure | [
"Kübra Bodur",
"Roxane Bertrand",
"James S. German",
"Stéphane Rauzy",
"Corinne Fredouille",
"Christine Meunier"
] | https://www.isca-archive.org/interspeech_2023/bodur23_interspeech.html | https://www.isca-archive.org/interspeech_2023/bodur23_interspeech.pdf | 10.21437/Interspeech.2023-1651 | 117-121 | @inproceedings{bodur23_interspeech,
title = {{Speech reduction: position within French prosodic structure}},
author = {Kübra Bodur and Roxane Bertrand and James S. German and Stéphane Rauzy and Corinne Fredouille and Christine Meunier},
year = {2023},
booktitle = {{Interspeech 2023}},
pages ... | Variation in the speech signal is a characteristic of spoken language, emerging partially as a result of interactions between various linguistic levels. One example of variation is phonetic reduction, where words are produced with missing or underspecified phonetic forms. Using a French conversational corpus, this pape... | null | null |
zhu23d_interspeech | Transvelar Nasal Coupling Contributing to Speaker Characteristics in Non-nasal Vowels | [
"Ziyu Zhu",
"Yujie Chi",
"Zhao Zhang",
"Kiyoshi Honda",
"Jianguo Wei"
] | https://www.isca-archive.org/interspeech_2023/zhu23d_interspeech.html | https://www.isca-archive.org/interspeech_2023/zhu23d_interspeech.pdf | 10.21437/Interspeech.2023-637 | 122-126 | @inproceedings{zhu23d_interspeech,
title = {{Transvelar Nasal Coupling Contributing to Speaker Characteristics in Non-nasal Vowels}},
author = {Ziyu Zhu and Yujie Chi and Zhao Zhang and Kiyoshi Honda and Jianguo Wei},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {122--126},
doi ... | Nasal-cavity structure is stable in speech and varied across speakers, which potentially gives rise to speaker characteristics. Many studies have reported the acoustic contribution of the nasal cavity for nasal and nasalized sounds with velopharyngeal port opening. However, nasal-cavity resonance does emerge in non-nas... | null | null |
otani23_interspeech | Speech Synthesis from Articulatory Movements Recorded by Real-time MRI | [
"Yuto Otani",
"Shun Sawada",
"Hidefumi Ohmura",
"Kouichi Katsurada"
] | https://www.isca-archive.org/interspeech_2023/otani23_interspeech.html | https://www.isca-archive.org/interspeech_2023/otani23_interspeech.pdf | 10.21437/Interspeech.2023-286 | 127-131 | @inproceedings{otani23_interspeech,
title = {{Speech Synthesis from Articulatory Movements Recorded by Real-time MRI}},
author = {Yuto Otani and Shun Sawada and Hidefumi Ohmura and Kouichi Katsurada},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {127--131},
doi = {10.21437/... | Previous speech synthesis models from articulatory movements recorded using real-time MRI (rtMRI) only predicted vocal tract shape parameters and required additional pitch information to generate a speech waveform. This study proposes a two-stage deep learning model composed of CNN-BiLSTM that predicts a mel-spectrogra... | null | null |
yuan23b_interspeech | The ART of Conversation: Measuring Phonetic Convergence and Deliberate Imitation in L2-Speech with a Siamese RNN | [
"Zheng Yuan",
"Aldo Pastore",
"Dorina de Jong",
"Hao Xu",
"Luciano Fadiga",
"Alessandro D'Ausilio"
] | https://www.isca-archive.org/interspeech_2023/yuan23b_interspeech.html | https://www.isca-archive.org/interspeech_2023/yuan23b_interspeech.pdf | 10.21437/Interspeech.2023-2283 | 132-136 | @inproceedings{yuan23b_interspeech,
title = {{The ART of Conversation: Measuring Phonetic Convergence and Deliberate Imitation in L2-Speech with a Siamese RNN}},
author = {Zheng Yuan and Aldo Pastore and Dorina {de Jong} and Hao Xu and Luciano Fadiga and Alessandro D'Ausilio},
year = {2023},
booktit... | Phonetic convergence describes the automatic and unconscious speech adaptation of two interlocutors in a conversation. This paper proposes a Siamese recurrent neural network (RNN) architecture to measure the convergence of the holistic spectral characteristics of speech sounds in an L2-L2 interaction. We extend an alte... | 2306.05088 | title_snapshot |
mahshie23_interspeech | Did you see that? Exploring the role of vision in the development of consonant feature contrasts in children with cochlear implants | [
"James Mahshie",
"Michael Larsen"
] | https://www.isca-archive.org/interspeech_2023/mahshie23_interspeech.html | https://www.isca-archive.org/interspeech_2023/mahshie23_interspeech.pdf | 10.21437/Interspeech.2023-1933 | 137-140 | @inproceedings{mahshie23_interspeech,
title = {{Did you see that? Exploring the role of vision in the development of consonant feature contrasts in children with cochlear implants}},
author = {James Mahshie and Michael Larsen},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {137--140... | This project aimed to explore the potential role of vision in speech contrast production and auditory perception development in children with cochlear implants (CWCI). Ten CWCI between 43 and 61 months of age, with at least 2 years of CI experience, served as participants. Employing an auditory imitation task, childre... | null | null |
vanbemmel23_interspeech | Automatic assessments of dysarthric speech: the usability of acoustic-phonetic features | [
"Loes van Bemmel",
"Chiara Pesenti",
"Xue Wei",
"Helmer Strik"
] | https://www.isca-archive.org/interspeech_2023/vanbemmel23_interspeech.html | https://www.isca-archive.org/interspeech_2023/vanbemmel23_interspeech.pdf | 10.21437/Interspeech.2023-2017 | 141-145 | @inproceedings{vanbemmel23_interspeech,
title = {{Automatic assessments of dysarthric speech: the usability of acoustic-phonetic features}},
author = {Loes {van Bemmel} and Chiara Pesenti and Xue Wei and Helmer Strik},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {141--145},
doi ... | Individuals with dysarthria suffer from difficulties in speech production and consequent reductions in speech intelligibility, which is an important concept for diagnosing and assessing effectiveness of speech therapy. In the current study, we investigate which acoustic-phonetic features are most relevant and important... | null | null |
venkatathirumalakumar23_interspeech | Classification of Multi-class Vowels and Fricatives From Patients Having Amyotrophic Lateral Sclerosis with Varied Levels of Dysarthria Severity | [
"Chowdam Venkata Thirumala Kumar",
"Tanuka Bhattacharjee",
"Yamini Belur",
"Atchayaram Nalini",
"Ravi Yadav",
"Prasanta Kumar Ghosh"
] | https://www.isca-archive.org/interspeech_2023/venkatathirumalakumar23_interspeech.html | https://www.isca-archive.org/interspeech_2023/venkatathirumalakumar23_interspeech.pdf | 10.21437/Interspeech.2023-1455 | 146-150 | @inproceedings{venkatathirumalakumar23_interspeech,
title = {{Classification of Multi-class Vowels and Fricatives From Patients Having Amyotrophic Lateral Sclerosis with Varied Levels of Dysarthria Severity}},
author = {Chowdam {Venkata Thirumala Kumar} and Tanuka Bhattacharjee and Yamini Belur and Atchayara... | Dysarthria due to Amyotrophic Lateral Sclerosis (ALS) progressively distorts the acoustic space affecting the discriminability of different vowels and fricatives. However, the extent to which this happens with increasing severity is not thoroughly investigated. In this work, we perform automatic 4-class vowel (/a/, /i/... | null | null |
qi23b_interspeech | Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation | [
"Jinzi Qi",
"Hugo Van hamme"
] | https://www.isca-archive.org/interspeech_2023/qi23b_interspeech.html | https://www.isca-archive.org/interspeech_2023/qi23b_interspeech.pdf | 10.21437/Interspeech.2023-1627 | 151-155 | @inproceedings{qi23b_interspeech,
title = {{Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation}},
author = {Jinzi Qi and Hugo {Van hamme}},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {151--155},
doi = {10.21437/Interspeec... | In dysarthric speech recognition, data scarcity and the vast diversity between dysarthric speakers pose significant challenges. While finetuning has been a popular solution, it can lead to overfitting and low parameter efficiency. Adapter modules offer a better solution, with their small size and easy applicability. Ad... | 2306.07090 | title_snapshot |
hermann23_interspeech | Few-shot Dysarthric Speech Recognition with Text-to-Speech Data Augmentation | [
"Enno Hermann",
"Mathew Magimai.-Doss"
] | https://www.isca-archive.org/interspeech_2023/hermann23_interspeech.html | https://www.isca-archive.org/interspeech_2023/hermann23_interspeech.pdf | 10.21437/Interspeech.2023-2481 | 156-160 | @inproceedings{hermann23_interspeech,
title = {{Few-shot Dysarthric Speech Recognition with Text-to-Speech Data Augmentation}},
author = {Enno Hermann and Mathew Magimai.-Doss},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {156--160},
doi = {10.21437/Interspeech.2023-2481},... | Speakers with dysarthria could particularly benefit from assistive speech technology, but are underserved by current automatic speech recognition (ASR) systems. The differences of dysarthric speech pose challenges, while recording large amounts of training data can be exhausting for patients. In this paper, we synthesi... | null | null |
yee23_interspeech | Latent Phrase Matching for Dysarthric Speech | [
"Dianna Yee",
"Colin Lea",
"Jaya Narain",
"Zifang Huang",
"Lauren Tooley",
"Jeffrey P. Bigham",
"Leah Findlater"
] | https://www.isca-archive.org/interspeech_2023/yee23_interspeech.html | https://www.isca-archive.org/interspeech_2023/yee23_interspeech.pdf | 10.21437/Interspeech.2023-1921 | 161-165 | @inproceedings{yee23_interspeech,
title = {{Latent Phrase Matching for Dysarthric Speech}},
author = {Dianna Yee and Colin Lea and Jaya Narain and Zifang Huang and Lauren Tooley and Jeffrey P. Bigham and Leah Findlater},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {161--165},
do... | Many consumer speech recognition systems are not tuned for people with speech disabilities, resulting in poor recognition and user experience, especially for severe speech differences. Recent studies has emphasized interest in designing and improving personalized speech models for atypical speech. We propose a query-b... | 2306.05446 | title_snapshot |
yeo23_interspeech | Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification | [
"Eun Jung Yeo",
"Kwanghee Choi",
"Sunhee Kim",
"Minhwa Chung"
] | https://www.isca-archive.org/interspeech_2023/yeo23_interspeech.html | https://www.isca-archive.org/interspeech_2023/yeo23_interspeech.pdf | 10.21437/Interspeech.2023-173 | 166-170 | @inproceedings{yeo23_interspeech,
title = {{Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification}},
author = {Eun Jung Yeo and Kwanghee Choi and Sunhee Kim and Minhwa Chung},
year = {2023},
booktitle = {{Interspeech 2023}},
pages... | This paper proposes an improved Goodness of Pronunciation (GoP) that utilizes Uncertainty Quantification (UQ) for automatic speech intelligibility assessment for dysarthric speech. Current GoP methods rely heavily on neural network-driven overconfident predictions, which is unsuitable for assessing dysarthric speech du... | 2305.18392 | title_snapshot |
zheng23c_interspeech | CQNV: A Combination of Coarsely Quantized Bitstream and Neural Vocoder for Low Rate Speech Coding | [
"Youqiang Zheng",
"Li Xiao",
"Weiping Tu",
"Yuhong Yang",
"Xinmeng Xu"
] | https://www.isca-archive.org/interspeech_2023/zheng23c_interspeech.html | https://www.isca-archive.org/interspeech_2023/zheng23c_interspeech.pdf | 10.21437/Interspeech.2023-1562 | 171-175 | @inproceedings{zheng23c_interspeech,
title = {{CQNV: A Combination of Coarsely Quantized Bitstream and Neural Vocoder for Low Rate Speech Coding}},
author = {Youqiang Zheng and Li Xiao and Weiping Tu and Yuhong Yang and Xinmeng Xu},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {171... | Recently, speech codecs based on neural networks have proven to perform better than traditional methods. However, redundancy in traditional parameter quantization is visible within the codec architecture of combining the traditional codec with the neural vocoder. In this paper, we propose a novel framework named CQNV, ... | 2307.13295 | title_snapshot |
kamo23_interspeech | Target Speech Extraction with Conditional Diffusion Model | [
"Naoyuki Kamo",
"Marc Delcroix",
"Tomohiro Nakatani"
] | https://www.isca-archive.org/interspeech_2023/kamo23_interspeech.html | https://www.isca-archive.org/interspeech_2023/kamo23_interspeech.pdf | 10.21437/Interspeech.2023-1234 | 176-180 | @inproceedings{kamo23_interspeech,
title = {{Target Speech Extraction with Conditional Diffusion Model}},
author = {Naoyuki Kamo and Marc Delcroix and Tomohiro Nakatani},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {176--180},
doi = {10.21437/Interspeech.2023-1234},
issn... | Diffusion model-based speech enhancement has received increased attention since it can generate very natural enhanced signals and generalizes well to unseen conditions. Diffusion models have been explored for several sub-tasks of speech enhancement, such as speech denoising, dereverberation, and source separation. In t... | 2308.03987 | title_snapshot |
cohen23_interspeech | Towards Fully Quantized Neural Networks For Speech Enhancement | [
"Elad Cohen",
"Hai Victor Habi",
"Arnon Netzer"
] | https://www.isca-archive.org/interspeech_2023/cohen23_interspeech.html | https://www.isca-archive.org/interspeech_2023/cohen23_interspeech.pdf | 10.21437/Interspeech.2023-883 | 181-185 | @inproceedings{cohen23_interspeech,
title = {{Towards Fully Quantized Neural Networks For Speech Enhancement}},
author = {Elad Cohen and Hai Victor Habi and Arnon Netzer},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {181--185},
doi = {10.21437/Interspeech.2023-883},
issn... | Deep learning models have shown state-of-the-art results in speech enhancement. However, deploying such models on an eight-bit integer-only device is challenging. In this work, we analyze the gaps in deploying a vanilla quantization-aware training method for speech enhancement, revealing two significant observations. F... | null | null |
zhang23p_interspeech | Complex Image Generation SwinTransformer Network for Audio Denoising | [
"Youshan Zhang",
"Jialu Li"
] | https://www.isca-archive.org/interspeech_2023/zhang23p_interspeech.html | https://www.isca-archive.org/interspeech_2023/zhang23p_interspeech.pdf | 10.21437/Interspeech.2023-980 | 186-190 | @inproceedings{zhang23p_interspeech,
title = {{Complex Image Generation SwinTransformer Network for Audio Denoising}},
author = {Youshan Zhang and Jialu Li},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {186--190},
doi = {10.21437/Interspeech.2023-980},
issn = {2958-... | Achieving high-performance audio denoising is still a challenging task in real-world applications. Existing time-frequency methods often ignore the quality of generated frequency domain images. This paper converts the audio denoising problem into an image generation task. We first develop a complex image generation Swi... | 2310.16109 | title_snapshot |
blau23_interspeech | Using Text Injection to Improve Recognition of Personal Identifiers in Speech | [
"Yochai Blau",
"Rohan Agrawal",
"Lior Madmony",
"Gary Wang",
"Andrew Rosenberg",
"Zhehuai Chen",
"Zorik Gekhman",
"Genady Beryozkin",
"Parisa Haghani",
"Bhuvana Ramabhadran"
] | https://www.isca-archive.org/interspeech_2023/blau23_interspeech.html | https://www.isca-archive.org/interspeech_2023/blau23_interspeech.pdf | 10.21437/Interspeech.2023-2118 | 191-195 | @inproceedings{blau23_interspeech,
title = {{Using Text Injection to Improve Recognition of Personal Identifiers in Speech}},
author = {Yochai Blau and Rohan Agrawal and Lior Madmony and Gary Wang and Andrew Rosenberg and Zhehuai Chen and Zorik Gekhman and Genady Beryozkin and Parisa Haghani and Bhuvana Rama... | Accurate recognition of specific categories, such as persons' names, dates or other identifiers is critical in many Automatic Speech Recognition (ASR) applications. As these categories represent personal information, ethical use of this data including collection, transcription, training and evaluation demands special c... | 2308.07393 | title_snapshot |
grosz23_interspeech | Investigating wav2vec2 context representations and the effects of fine-tuning, a case-study of a Finnish model | [
"Tamas Grosz",
"Yaroslav Getman",
"Ragheb Al-Ghezi",
"Aku Rouhe",
"Mikko Kurimo"
] | https://www.isca-archive.org/interspeech_2023/grosz23_interspeech.html | https://www.isca-archive.org/interspeech_2023/grosz23_interspeech.pdf | 10.21437/Interspeech.2023-837 | 196-200 | @inproceedings{grosz23_interspeech,
title = {{Investigating wav2vec2 context representations and the effects of fine-tuning, a case-study of a Finnish model}},
author = {Tamas Grosz and Yaroslav Getman and Ragheb Al-Ghezi and Aku Rouhe and Mikko Kurimo},
year = {2023},
booktitle = {{Interspeech 2023... | Self-supervised speech models, such as the wav2vec2, have become extremely popular in the past few years. Their main appeal is that after their pre-training on a large amount of audio, they require only a small amount of supervised, finetuning data to achieve outstanding results. Despite their immense success, very lit... | null | null |
lehecka23_interspeech | Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech | [
"Jan LeheÄka",
"Jan Å vec",
"Josef V. Psutka",
"Pavel Ircing"
] | https://www.isca-archive.org/interspeech_2023/lehecka23_interspeech.html | https://www.isca-archive.org/interspeech_2023/lehecka23_interspeech.pdf | 10.21437/Interspeech.2023-872 | 201-205 | @inproceedings{lehecka23_interspeech,
title = {{Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech}},
author = {Jan LeheÄka and Jan Å vec and Josef V. Psutka and Pavel Ircing},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {201--205}... | This paper is a step forward in our effort to make vast oral history archives more accessible to the public and researchers by breaking down the decoding barriers between the knowledge encoded in the spoken testimonies and users who want to search for the information of their interest. We present new Transformer-based ... | null | null |
singh23_interspeech | Iteratively Improving Speech Recognition and Voice Conversion | [
"Mayank Kumar Singh",
"Naoya Takahashi",
"Naoyuki Onoe"
] | https://www.isca-archive.org/interspeech_2023/singh23_interspeech.html | https://www.isca-archive.org/interspeech_2023/singh23_interspeech.pdf | 10.21437/Interspeech.2023-177 | 206-210 | @inproceedings{singh23_interspeech,
title = {{Iteratively Improving Speech Recognition and Voice Conversion}},
author = {Mayank Kumar Singh and Naoya Takahashi and Naoyuki Onoe},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {206--210},
doi = {10.21437/Interspeech.2023-177},... | Many existing works on voice conversion (VC) tasks use automatic speech recognition (ASR) models for ensuring linguistic consistency between source and converted samples. However, for the low-data resource domains, training a high-quality ASR remains to be a challenging task. In this work, we propose a novel iterative ... | 2305.15055 | title_snapshot |
fatehi23_interspeech | LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems | [
"Kavan Fatehi",
"Ayse Kucukyilmaz"
] | https://www.isca-archive.org/interspeech_2023/fatehi23_interspeech.html | https://www.isca-archive.org/interspeech_2023/fatehi23_interspeech.pdf | 10.21437/Interspeech.2023-2001 | 211-215 | @inproceedings{fatehi23_interspeech,
title = {{LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems}},
author = {Kavan Fatehi and Ayse Kucukyilmaz},
year = {2023},
booktitle = {{Interspeech 20... | With advances in deep learning methodologies, Automatic Speech Recognition (ASR) systems have seen impressive results. However, ASR in Low-Resource Environments (LREs) are challenged by a lack of training data for the specific target domain. We propose that data sampling criteria for choosing more informative speech sa... | null | null |
xue23_interspeech | TranUSR: Phoneme-to-word Transcoder Based Unified Speech Representation Learning for Cross-lingual Speech Recognition | [
"Hongfei Xue",
"Qijie Shao",
"Peikun Chen",
"Pengcheng Guo",
"Lei Xie",
"Jie Liu"
] | https://www.isca-archive.org/interspeech_2023/xue23_interspeech.html | https://www.isca-archive.org/interspeech_2023/xue23_interspeech.pdf | 10.21437/Interspeech.2023-746 | 216-220 | @inproceedings{xue23_interspeech,
title = {{TranUSR: Phoneme-to-word Transcoder Based Unified Speech Representation Learning for Cross-lingual Speech Recognition}},
author = {Hongfei Xue and Qijie Shao and Peikun Chen and Pengcheng Guo and Lei Xie and Jie Liu},
year = {2023},
booktitle = {{Interspee... | UniSpeech has achieved superior performance in cross-lingual automatic speech recognition (ASR) by explicitly aligning latent representations to phoneme units using multi-task self-supervised learning. While the learned representations transfer well from high-resource to low-resource languages, predicting words directl... | 2305.13629 | title_snapshot |
wu23e_interspeech | Dual-Mode NAM: Effective Top-K Context Injection for End-to-End ASR | [
"Zelin Wu",
"Tsendsuren Munkhdalai",
"Pat Rondon",
"Golan Pundak",
"Khe Chai Sim",
"Christopher Li"
] | https://www.isca-archive.org/interspeech_2023/wu23e_interspeech.html | https://www.isca-archive.org/interspeech_2023/wu23e_interspeech.pdf | 10.21437/Interspeech.2023-1124 | 221-225 | @inproceedings{wu23e_interspeech,
title = {{Dual-Mode NAM: Effective Top-K Context Injection for End-to-End ASR}},
author = {Zelin Wu and Tsendsuren Munkhdalai and Pat Rondon and Golan Pundak and Khe Chai Sim and Christopher Li},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {221--2... | ASR systems in real applications must be adapted on the fly to correctly recognize task-specific contextual terms, such as contacts, application names and media entities. However, it is challenging to achieve scalability, large in-domain quality gains, and minimal out-of-domain quality regressions simultaneously. In th... | null | null |
zhou23g_interspeech | GhostRNN: Reducing State Redundancy in RNN with Cheap Operations | [
"Hang Zhou",
"Xiaoxu Zheng",
"Yunhe Wang",
"Michael Bi Mi",
"Deyi Xiong",
"Kai Han"
] | https://www.isca-archive.org/interspeech_2023/zhou23g_interspeech.html | https://www.isca-archive.org/interspeech_2023/zhou23g_interspeech.pdf | 10.21437/Interspeech.2023-2417 | 226-230 | @inproceedings{zhou23g_interspeech,
title = {{GhostRNN: Reducing State Redundancy in RNN with Cheap Operations}},
author = {Hang Zhou and Xiaoxu Zheng and Yunhe Wang and Michael Bi Mi and Deyi Xiong and Kai Han},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {226--230},
doi ... | Recurrent neural network (RNNs) that are capable of modeling long-distance dependencies are widely used in various speech tasks, eg., keyword spotting (KWS) and speech enhancement (SE). Due to the limitation of power and memory in low-resource devices, efficient RNN models are urgently required for real-world applicati... | 2411.14489 | title_snapshot |
wang23da_interspeech | Task-Agnostic Structured Pruning of Speech Representation Models | [
"Haoyu Wang",
"Siyuan Wang",
"Wei-Qiang Zhang",
"Suo Hongbin",
"Yulong Wan"
] | https://www.isca-archive.org/interspeech_2023/wang23da_interspeech.html | https://www.isca-archive.org/interspeech_2023/wang23da_interspeech.pdf | 10.21437/Interspeech.2023-1442 | 231-235 | @inproceedings{wang23da_interspeech,
title = {{Task-Agnostic Structured Pruning of Speech Representation Models}},
author = {Haoyu Wang and Siyuan Wang and Wei-Qiang Zhang and Suo Hongbin and Yulong Wan},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {231--235},
doi = {10.21... | Self-supervised pre-trained models such as Wav2vec2, Hubert, and WavLM have been shown to significantly improve many speech tasks. However, their large memory and strong computational requirements hinder their industrial applicability. Structured pruning is a hardware-friendly model compression technique but usually re... | 2306.01385 | title_snapshot |
kanda23_interspeech | Factual Consistency Oriented Speech Recognition | [
"Naoyuki Kanda",
"Takuya Yoshioka",
"Yang Liu"
] | https://www.isca-archive.org/interspeech_2023/kanda23_interspeech.html | https://www.isca-archive.org/interspeech_2023/kanda23_interspeech.pdf | 10.21437/Interspeech.2023-485 | 236-240 | @inproceedings{kanda23_interspeech,
title = {{Factual Consistency Oriented Speech Recognition}},
author = {Naoyuki Kanda and Takuya Yoshioka and Yang Liu},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {236--240},
doi = {10.21437/Interspeech.2023-485},
issn = {2958-17... | This paper presents a novel optimization framework for automatic speech recognition (ASR) with the aim of reducing hallucinations produced by an ASR model. The proposed framework optimizes the ASR model to maximize an expected factual consistency score between ASR hypotheses and ground-truth transcriptions, where the f... | 2302.12369 | title_snapshot |
fathullah23_interspeech | Multi-Head State Space Model for Speech Recognition | [
"Yassir Fathullah",
"Chunyang Wu",
"Yuan Shangguan",
"Junteng Jia",
"Wenhan Xiong",
"Jay Mahadeokar",
"Chunxi Liu",
"Yangyang Shi",
"Ozlem Kalinli",
"Mike Seltzer",
"Mark J. F. Gales"
] | https://www.isca-archive.org/interspeech_2023/fathullah23_interspeech.html | https://www.isca-archive.org/interspeech_2023/fathullah23_interspeech.pdf | 10.21437/Interspeech.2023-1036 | 241-245 | @inproceedings{fathullah23_interspeech,
title = {{Multi-Head State Space Model for Speech Recognition}},
author = {Yassir Fathullah and Chunyang Wu and Yuan Shangguan and Junteng Jia and Wenhan Xiong and Jay Mahadeokar and Chunxi Liu and Yangyang Shi and Ozlem Kalinli and Mike Seltzer and Mark J. F. Gales},
... | State space models (SSMs) have recently shown promising results on small-scale sequence and language modelling tasks, rivalling and outperforming many attention-based approaches. In this paper, we propose a multi-head state space (MH-SSM) architecture equipped with special gating mechanisms, where parallel heads are ta... | 2305.12498 | title_snapshot |
gao23_interspeech | Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search | [
"Yingying Gao",
"Shilei Zhang",
"Zihao Cui",
"Chao Deng",
"Junlan Feng"
] | https://www.isca-archive.org/interspeech_2023/gao23_interspeech.html | https://www.isca-archive.org/interspeech_2023/gao23_interspeech.pdf | 10.21437/Interspeech.2023-341 | 246-250 | @inproceedings{gao23_interspeech,
title = {{Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search}},
author = {Yingying Gao and Shilei Zhang and Zihao Cui and Chao Deng and Junlan Feng},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {246--250},
doi = {10.... | Cascading multiple pre-trained models is an effective way to compose an end-to-end system. However, fine-tuning the full cascaded model is parameter and memory inefficient and our observations reveal that only applying adapter modules on cascaded model can not achieve considerable performance as fine-tuning. We propose... | 2310.17664 | title_snapshot |
martin23_interspeech | Probing Self-supervised Speech Models for Phonetic and Phonemic Information: A Case Study in Aspiration | [
"Kinan Martin",
"Jon Gauthier",
"Canaan Breiss",
"Roger Levy"
] | https://www.isca-archive.org/interspeech_2023/martin23_interspeech.html | https://www.isca-archive.org/interspeech_2023/martin23_interspeech.pdf | 10.21437/Interspeech.2023-2359 | 251-255 | @inproceedings{martin23_interspeech,
title = {{Probing Self-supervised Speech Models for Phonetic and Phonemic Information: A Case Study in Aspiration}},
author = {Kinan Martin and Jon Gauthier and Canaan Breiss and Roger Levy},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {251--25... | Textless self-supervised speech models have grown in capabilities in recent years, but the nature of the linguistic information they encode has not yet been thoroughly examined. We evaluate the extent to which these models' learned representations align with basic representational distinctions made by humans, focusing ... | 2306.06232 | title_snapshot |
harding23_interspeech | Selective Biasing with Trie-based Contextual Adapters for Personalised Speech Recognition using Neural Transducers | [
"Philip Harding",
"Sibo Tong",
"Simon Wiesler"
] | https://www.isca-archive.org/interspeech_2023/harding23_interspeech.html | https://www.isca-archive.org/interspeech_2023/harding23_interspeech.pdf | 10.21437/Interspeech.2023-739 | 256-260 | @inproceedings{harding23_interspeech,
title = {{Selective Biasing with Trie-based Contextual Adapters for Personalised Speech Recognition using Neural Transducers}},
author = {Philip Harding and Sibo Tong and Simon Wiesler},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {256--260},
... | Neural transducer ASR models achieve state of the art accuracy on many tasks, however rare word recognition poses a particular challenge as models often fail to recognise words that occur rarely, or not at all, in the training data. Methods of contextual biasing, where models are dynamically adapted to bias their outpu... | null | null |
zeng23b_interspeech | Robust Prototype Learning for Anomalous Sound Detection | [
"Xiao-Min Zeng",
"Yan Song",
"Ian McLoughlin",
"Lin Liu",
"Li-Rong Dai"
] | https://www.isca-archive.org/interspeech_2023/zeng23b_interspeech.html | https://www.isca-archive.org/interspeech_2023/zeng23b_interspeech.pdf | 10.21437/Interspeech.2023-1173 | 261-265 | @inproceedings{zeng23b_interspeech,
title = {{Robust Prototype Learning for Anomalous Sound Detection}},
author = {Xiao-Min Zeng and Yan Song and Ian McLoughlin and Lin Liu and Li-Rong Dai},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {261--265},
doi = {10.21437/Interspeec... | In this paper, we present a robust prototype learning framework for anomalous sound detection (ASD), where prototypical loss is exploited to measure the similarity between samples and prototypes. We show that existing generative and discriminative based ASD methods can be unified into this framework from the perspectiv... | null | null |
kushwaha23_interspeech | A multimodal prototypical approach for unsupervised sound classification | [
"Saksham Singh Kushwaha",
"Magdalena Fuentes"
] | https://www.isca-archive.org/interspeech_2023/kushwaha23_interspeech.html | https://www.isca-archive.org/interspeech_2023/kushwaha23_interspeech.pdf | 10.21437/Interspeech.2023-982 | 266-270 | @inproceedings{kushwaha23_interspeech,
title = {{A multimodal prototypical approach for unsupervised sound classification}},
author = {Saksham Singh Kushwaha and Magdalena Fuentes},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {266--270},
doi = {10.21437/Interspeech.2023-98... | In the context of environmental sound classification, the adaptability of systems is key: which sound classes are interesting depends on the context and the user's needs. Recent advances in text-to-audio retrieval allow for zero-shot audio classification, but performance compared to supervised models remains limited. T... | 2306.12300 | title_snapshot |
wen23_interspeech | Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms | [
"Penghui Wen",
"Kun Hu",
"Wenxi Yue",
"Sen Zhang",
"Wanlei Zhou",
"Zhiyong Wang"
] | https://www.isca-archive.org/interspeech_2023/wen23_interspeech.html | https://www.isca-archive.org/interspeech_2023/wen23_interspeech.pdf | 10.21437/Interspeech.2023-563 | 271-275 | @inproceedings{wen23_interspeech,
title = {{Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms}},
author = {Penghui Wen and Kun Hu and Wenxi Yue and Sen Zhang and Wanlei Zhou and Zhiyong Wang},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {27... | Robust audio anti-spoofing has been increasingly challeng- ing due to the recent advancements on deepfake techniques. While spectrograms have demonstrated their capability for anti- spoofing, complementary information presented in multi-order spectral patterns have not been well explored, which limits their effectivene... | 2308.09302 | title_snapshot |
liang23c_interspeech | Adapting Language-Audio Models as Few-Shot Audio Learners | [
"Jinhua Liang",
"Xubo Liu",
"Haohe Liu",
"Huy Phan",
"Emmanouil Benetos",
"Mark D. Plumbley",
"Wenwu Wang"
] | https://www.isca-archive.org/interspeech_2023/liang23c_interspeech.html | https://www.isca-archive.org/interspeech_2023/liang23c_interspeech.pdf | 10.21437/Interspeech.2023-1082 | 276-280 | @inproceedings{liang23c_interspeech,
title = {{Adapting Language-Audio Models as Few-Shot Audio Learners}},
author = {Jinhua Liang and Xubo Liu and Haohe Liu and Huy Phan and Emmanouil Benetos and Mark D. Plumbley and Wenwu Wang},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {276--... | Contrastive language-audio pretraining (CLAP) has become a new paradigm to learn audio concepts with audio-text pairs. CLAP models have shown unprecedented performance as zero-shot classifiers on downstream tasks. To further adapt CLAP with domain-specific knowledge, a popular method is to finetune its audio encoder wi... | 2305.17719 | title_snapshot |
wang23l_interspeech | TFECN: Time-Frequency Enhanced ConvNet for Audio Classification | [
"Mengwei Wang",
"Zhe Yang"
] | https://www.isca-archive.org/interspeech_2023/wang23l_interspeech.html | https://www.isca-archive.org/interspeech_2023/wang23l_interspeech.pdf | 10.21437/Interspeech.2023-734 | 281-285 | @inproceedings{wang23l_interspeech,
title = {{TFECN: Time-Frequency Enhanced ConvNet for Audio Classification}},
author = {Mengwei Wang and Zhe Yang},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {281--285},
doi = {10.21437/Interspeech.2023-734},
issn = {2958-1796},
... | Recently, transformer-based models have shown leading performance in audio classification, gradually replacing the dominant ConvNet in the past. However, some research has shown that certain characteristics and designs in transformers can be applied to other architectures and make them achieve similar performance as tr... | null | null |
choi23b_interspeech | Resolution Consistency Training on Time-Frequency Domain for Semi-Supervised Sound Event Detection | [
"Won-Gook Choi",
"Joon-Hyuk Chang"
] | https://www.isca-archive.org/interspeech_2023/choi23b_interspeech.html | https://www.isca-archive.org/interspeech_2023/choi23b_interspeech.pdf | 10.21437/Interspeech.2023-350 | 286-290 | @inproceedings{choi23b_interspeech,
title = {{Resolution Consistency Training on Time-Frequency Domain for Semi-Supervised Sound Event Detection}},
author = {Won-Gook Choi and Joon-Hyuk Chang},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {286--290},
doi = {10.21437/Intersp... | The fact that unlabeled data can be used for supervised learning is of considerable relevance concerning polyphonic sound event detection (PSED) because of the high costs of frame-wise labeling. While semi-supervised learning (SSL) for image tasks has been extensively developed, SSL for PSED has not been substantially ... | null | null |
li23n_interspeech | Fine-tuning Audio Spectrogram Transformer with Task-aware Adapters for Sound Event Detection | [
"Kang Li",
"Yan Song",
"Ian McLoughlin",
"Lin Liu",
"Jin Li",
"Li-Rong Dai"
] | https://www.isca-archive.org/interspeech_2023/li23n_interspeech.html | https://www.isca-archive.org/interspeech_2023/li23n_interspeech.pdf | 10.21437/Interspeech.2023-1174 | 291-295 | @inproceedings{li23n_interspeech,
title = {{Fine-tuning Audio Spectrogram Transformer with Task-aware Adapters for Sound Event Detection}},
author = {Kang Li and Yan Song and Ian McLoughlin and Lin Liu and Jin Li and Li-Rong Dai},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {291--... | In this paper, we present a task-aware fine-tuning method to transfer Patchout faSt Spectrogram Transformer (PaSST) model to sound event detection (SED) task. Pretrained PaSST has shown significant performance on audio tagging (AT) and SED tasks, but it is not optimal to fine-tune the model from a single layer as the l... | null | null |
ng23b_interspeech | Small Footprint Multi-channel Network for Keyword Spotting with Centroid Based Awareness | [
"Dianwen Ng",
"Yang Xiao",
"Jia Qi Yip",
"Zhao Yang",
"Biao Tian",
"Qiang Fu",
"Eng Siong Chng",
"Bin Ma"
] | https://www.isca-archive.org/interspeech_2023/ng23b_interspeech.html | https://www.isca-archive.org/interspeech_2023/ng23b_interspeech.pdf | 10.21437/Interspeech.2023-1210 | 296-300 | @inproceedings{ng23b_interspeech,
title = {{Small Footprint Multi-channel Network for Keyword Spotting with Centroid Based Awareness}},
author = {Dianwen Ng and Yang Xiao and Jia Qi Yip and Zhao Yang and Biao Tian and Qiang Fu and Eng Siong Chng and Bin Ma},
year = {2023},
booktitle = {{Interspeech ... | Spoken Keyword Spotting (KWS) in noisy far-field environments is challenging for small-footprint models, given the restrictions on computational resources (e.g., model size, running memory). This is even more intricate when handling noises from multiple microphones. To address this, we present a new multi-channel model... | 2204.05445 | title_judge |
xie23b_interspeech | Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes | [
"Wei Xie",
"Yanxiong Li",
"Qianhua He",
"Wenchang Cao",
"Tuomas Virtanen"
] | https://www.isca-archive.org/interspeech_2023/xie23b_interspeech.html | https://www.isca-archive.org/interspeech_2023/xie23b_interspeech.pdf | 10.21437/Interspeech.2023-1380 | 301-305 | @inproceedings{xie23b_interspeech,
title = {{Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes}},
author = {Wei Xie and Yanxiong Li and Qianhua He and Wenchang Cao and Tuomas Virtanen},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {301--305},
doi... | New classes of sounds constantly emerge with a few samples, making it challenging for models to adapt to dynamic acoustic environments. This challenge motivates us to address the new problem of few-shot class-incremental audio classification. This study aims to enable a model to continuously recognize new classes of so... | 2305.18045 | title_snapshot |
vali23_interspeech | Interpretable Latent Space Using Space-Filling Curves for Phonetic Analysis in Voice Conversion | [
"Mohammad Hassan Vali",
"Tom Bäckström"
] | https://www.isca-archive.org/interspeech_2023/vali23_interspeech.html | https://www.isca-archive.org/interspeech_2023/vali23_interspeech.pdf | 10.21437/Interspeech.2023-1549 | 306-310 | @inproceedings{vali23_interspeech,
title = {{Interpretable Latent Space Using Space-Filling Curves for Phonetic Analysis in Voice Conversion}},
author = {Mohammad Hassan Vali and Tom Bäckström},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {306--310},
doi = {10.21437/Inte... | Vector quantized variational autoencoders (VQ-VAE) are well-known deep generative models, which map input data to a latent space that is used for data generation. Such latent spaces are unstructured and can thus be difficult to interpret. Some earlier approaches have introduced a structure to the latent space through s... | null | null |
tulchinskii23_interspeech | Topological Data Analysis for Speech Processing | [
"Eduard Tulchinskii",
"Kristian Kuznetsov",
"Laida Kushnareva",
"Daniil Cherniavskii",
"Serguei Barannikov",
"Irina Piontkovskaya",
"Sergey Nikolenko",
"Evgeny Burnaev"
] | https://www.isca-archive.org/interspeech_2023/tulchinskii23_interspeech.html | https://www.isca-archive.org/interspeech_2023/tulchinskii23_interspeech.pdf | 10.21437/Interspeech.2023-1861 | 311-315 | @inproceedings{tulchinskii23_interspeech,
title = {{Topological Data Analysis for Speech Processing}},
author = {Eduard Tulchinskii and Kristian Kuznetsov and Laida Kushnareva and Daniil Cherniavskii and Serguei Barannikov and Irina Piontkovskaya and Sergey Nikolenko and Evgeny Burnaev},
year = {2023}... | We apply topological data analysis (TDA) to speech classification problems and to the introspection of a pretrained speech model, HuBERT. To this end, we introduce a number of topological and algebraic features derived from Transformer attention maps and embeddings. We show that a simple linear classifier built on top ... | 2211.17223 | title_snapshot |
jang23_interspeech | Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation | [
"Kangwook Jang",
"Sungnyun Kim",
"Se-Young Yun",
"Hoirin Kim"
] | https://www.isca-archive.org/interspeech_2023/jang23_interspeech.html | https://www.isca-archive.org/interspeech_2023/jang23_interspeech.pdf | 10.21437/Interspeech.2023-1329 | 316-320 | @inproceedings{jang23_interspeech,
title = {{Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation}},
author = {Kangwook Jang and Sungnyun Kim and Se-Young Yun and Hoirin Kim},
year = {2023},
booktitle = {{Inte... | Transformer-based speech self-supervised learning (SSL) models, such as HuBERT, show surprising performance in various speech processing tasks. However, huge number of parameters in speech SSL models necessitate the compression to a more compact model for wider usage in academia or small companies. In this study, we su... | 2305.11685 | title_snapshot |
koppelmann23_interspeech | Personalized Acoustic Scene Classification in Ultra-low Power Embedded Devices Using Privacy-preserving Data Augmentation | [
"Timm Koppelmann",
"Semih Agcaer",
"Rainer Martin"
] | https://www.isca-archive.org/interspeech_2023/koppelmann23_interspeech.html | https://www.isca-archive.org/interspeech_2023/koppelmann23_interspeech.pdf | 10.21437/Interspeech.2023-932 | 321-325 | @inproceedings{koppelmann23_interspeech,
title = {{Personalized Acoustic Scene Classification in Ultra-low Power Embedded Devices Using Privacy-preserving Data Augmentation}},
author = {Timm Koppelmann and Semih Agcaer and Rainer Martin},
year = {2023},
booktitle = {{Interspeech 2023}},
pages ... | In this work we present an adaptation method for personalized acoustic scene classification in ultra-low power embedded devices (EDs). The computational limitation of EDs and a large variety of acoustic scenes may lead to poor performance of the embedded classifier in specific real-world user environments. We propose a... | null | null |
lin23_interspeech | Background Domain Switch: A Novel Data Augmentation Technique for Robust Sound Event Detection | [
"Wei-Cheng Lin",
"Luca Bondi",
"Shabnam Ghaffarzadegan"
] | https://www.isca-archive.org/interspeech_2023/lin23_interspeech.html | https://www.isca-archive.org/interspeech_2023/lin23_interspeech.pdf | 10.21437/Interspeech.2023-176 | 326-330 | @inproceedings{lin23_interspeech,
title = {{Background Domain Switch: A Novel Data Augmentation Technique for Robust Sound Event Detection}},
author = {Wei-Cheng Lin and Luca Bondi and Shabnam Ghaffarzadegan},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {326--330},
doi = {... | Data augmentation is a key component to achieve robust and generalizable performance in sound event detection (SED). A well trained SED model should be able to resist the interference of non-target audio events and maintain a robust recognition rate under unknown and possibly mismatched testing conditions. In this stud... | null | null |
hou23_interspeech | Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning | [
"Yuanbo Hou",
"Siyang Song",
"Cheng Luo",
"Andrew Mitchell",
"Qiaoqiao Ren",
"Weicheng Xie",
"Jian Kang",
"Wenwu Wang",
"Dick Botteldooren"
] | https://www.isca-archive.org/interspeech_2023/hou23_interspeech.html | https://www.isca-archive.org/interspeech_2023/hou23_interspeech.pdf | 10.21437/Interspeech.2023-1021 | 331-335 | @inproceedings{hou23_interspeech,
title = {{Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning}},
author = {Yuanbo Hou and Siyang Song and Cheng Luo and Andrew Mitchell and Qiaoqiao Ren and Weicheng Xie and Jian Kang and Wenwu Wang and Di... | Sound events in daily life carry rich information about the objective world. The composition of these sounds affects the mood of people in a soundscape. Most previous approaches only focus on classifying and detecting audio events and scenes, but may ignore their perceptual quality that may impact humans' listening moo... | 2308.11980 | title_snapshot |
zhang23fa_interspeech | Anomalous Sound Detection Using Self-Attention-Based Frequency Pattern Analysis of Machine Sounds | [
"Hejing Zhang",
"Jian Guan",
"Qiaoxi Zhu",
"Feiyang Xiao",
"Youde Liu"
] | https://www.isca-archive.org/interspeech_2023/zhang23fa_interspeech.html | https://www.isca-archive.org/interspeech_2023/zhang23fa_interspeech.pdf | 10.21437/Interspeech.2023-2416 | 336-340 | @inproceedings{zhang23fa_interspeech,
title = {{Anomalous Sound Detection Using Self-Attention-Based Frequency Pattern Analysis of Machine Sounds}},
author = {Hejing Zhang and Jian Guan and Qiaoxi Zhu and Feiyang Xiao and Youde Liu},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {33... | Different machines can exhibit diverse frequency patterns in their emitted sound. This feature has been recently explored in anomaly sound detection and reached state-of-the-art performance. However, existing methods rely on the manual or empirical determination of the frequency filter by observing the effective freque... | 2308.14063 | title_snapshot |
xin23c_interspeech | Improving Audio-Text Retrieval via Hierarchical Cross-Modal Interaction and Auxiliary Captions | [
"Yifei Xin",
"Yuexian Zou"
] | https://www.isca-archive.org/interspeech_2023/xin23c_interspeech.html | https://www.isca-archive.org/interspeech_2023/xin23c_interspeech.pdf | 10.21437/Interspeech.2023-1478 | 341-345 | @inproceedings{xin23c_interspeech,
title = {{Improving Audio-Text Retrieval via Hierarchical Cross-Modal Interaction and Auxiliary Captions}},
author = {Yifei Xin and Yuexian Zou},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {341--345},
doi = {10.21437/Interspeech.2023-147... | Most existing audio-text retrieval (ATR) methods focus on constructing contrastive pairs between whole audio clips and complete caption sentences, while ignoring fine-grained crossmodal relationships, e.g., short segments and phrases or frames and words. In this paper, we introduce a hierarchical crossmodal interaction... | 2307.15344 | title_snapshot |
bn23_interspeech | Differential Privacy enabled Dementia Classification: An Exploration of the Privacy-Accuracy Trade-off in Speech Signal Data | [
"Suhas BN",
"Sarah Rajtmajer",
"Saeed Abdullah"
] | https://www.isca-archive.org/interspeech_2023/bn23_interspeech.html | https://www.isca-archive.org/interspeech_2023/bn23_interspeech.pdf | 10.21437/Interspeech.2023-575 | 346-350 | @inproceedings{bn23_interspeech,
title = {{Differential Privacy enabled Dementia Classification: An Exploration of the Privacy-Accuracy Trade-off in Speech Signal Data}},
author = {Suhas BN and Sarah Rajtmajer and Saeed Abdullah},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {346--... | Early detection of dementia is critical for effective symptom management. Recent studies have aimed to develop machine learning (ML) models to identify dementia onset and severity using language and speech features. However, existing methods can lead to serious privacy concerns due to sensitive data collected from a vu... | null | null |
wang23ka_interspeech | Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech | [
"Shijun Wang",
"Jón Guðnason",
"Damian Borth"
] | https://www.isca-archive.org/interspeech_2023/wang23ka_interspeech.html | https://www.isca-archive.org/interspeech_2023/wang23ka_interspeech.pdf | 10.21437/Interspeech.2023-1595 | 351-355 | @inproceedings{wang23ka_interspeech,
title = {{Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech}},
author = {Shijun Wang and Jón Guðnason and Damian Borth},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {351... | Effective speech emotional representations play a key role in Speech Emotion Recognition (SER) and Emotional Text-To-Speech (TTS) tasks. However, emotional speech samples are more difficult and expensive to acquire compared with Neutral style speech, which causes one issue that most related works unfortunately neglect:... | 2306.05709 | title_snapshot |
behera23_interspeech | Towards Multi-Lingual Audio Question Answering | [
"Swarup Ranjan Behera",
"Pailla Balakrishna Reddy",
"Achyut Mani Tripathi",
"Megavath Bharadwaj Rathod",
"Tejesh Karavadi"
] | https://www.isca-archive.org/interspeech_2023/behera23_interspeech.html | https://www.isca-archive.org/interspeech_2023/behera23_interspeech.pdf | 10.21437/Interspeech.2023-1816 | 356-360 | @inproceedings{behera23_interspeech,
title = {{Towards Multi-Lingual Audio Question Answering}},
author = {Swarup Ranjan Behera and Pailla Balakrishna Reddy and Achyut Mani Tripathi and Megavath Bharadwaj Rathod and Tejesh Karavadi},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {35... | Audio Question Answering (AQA) is a multi-modal translation task where a system analyzes an audio signal and a natural language question to generate a desirable natural language answer. AQA has been primarily studied through the lens of the English language. However, addressing AQA in other languages, in the same manne... | null | null |
aldarmaki23_interspeech | Diacritic Recognition Performance in Arabic ASR | [
"Hanan Aldarmaki",
"Ahmad Ghannam"
] | https://www.isca-archive.org/interspeech_2023/aldarmaki23_interspeech.html | https://www.isca-archive.org/interspeech_2023/aldarmaki23_interspeech.pdf | 10.21437/Interspeech.2023-2344 | 361-365 | @inproceedings{aldarmaki23_interspeech,
title = {{Diacritic Recognition Performance in Arabic ASR}},
author = {Hanan Aldarmaki and Ahmad Ghannam},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {361--365},
doi = {10.21437/Interspeech.2023-2344},
issn = {2958-1796},
} | In Arabic text, most vowels are encoded in the form of diacritics that are often omitted, so most speech corpora and ASR models are undiacritized. Text-based diacritization has previously been used to preprocess the input or post-processs ASR hypotheses. It is generally believed that input diacritization degrades ASR q... | 2302.14022 | title_snapshot |
kolehmainen23_interspeech | Personalization for BERT-based Discriminative Speech Recognition Rescoring | [
"Jari Kolehmainen",
"Yile Gu",
"Aditya Gourav",
"Prashanth Gurunath Shivakumar",
"Ankur Gandhe",
"Ariya Rastrow",
"Ivan Bulyko"
] | https://www.isca-archive.org/interspeech_2023/kolehmainen23_interspeech.html | https://www.isca-archive.org/interspeech_2023/kolehmainen23_interspeech.pdf | 10.21437/Interspeech.2023-990 | 366-370 | @inproceedings{kolehmainen23_interspeech,
title = {{Personalization for BERT-based Discriminative Speech Recognition Rescoring}},
author = {Jari Kolehmainen and Yile Gu and Aditya Gourav and Prashanth {Gurunath Shivakumar} and Ankur Gandhe and Ariya Rastrow and Ivan Bulyko},
year = {2023},
booktitle... | Recognition of personalized content remains a challenge in end-to-end speech recognition. We explore three novel approaches that use personalized content in a neural rescoring step to improve recognition: gazetteers, prompting, and a cross-attention based encoder-decoder model. We use internal de-identified en-US data ... | 2307.06832 | title_snapshot |
krishnan23_interspeech | On the N-gram Approximation of Pre-trained Language Models | [
"Aravind Krishnan",
"Jesujoba O. Alabi",
"Dietrich Klakow"
] | https://www.isca-archive.org/interspeech_2023/krishnan23_interspeech.html | https://www.isca-archive.org/interspeech_2023/krishnan23_interspeech.pdf | 10.21437/Interspeech.2023-2182 | 371-375 | @inproceedings{krishnan23_interspeech,
title = {{On the N-gram Approximation of Pre-trained Language Models}},
author = {Aravind Krishnan and Jesujoba O. Alabi and Dietrich Klakow},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {371--375},
doi = {10.21437/Interspeech.2023-21... | Large pre-trained language models (PLMs) have shown remarkable performance across various natural language understanding (NLU) tasks, particularly in low-resource settings. Nevertheless, their potential in Automatic Speech Recognition (ASR) remains largely unexplored. This study investigates the potential usage of PLMs... | 2306.06892 | title_snapshot |
huang23g_interspeech | Record Deduplication for Entity Distribution Modeling in ASR Transcripts | [
"Tianyu Huang",
"Chung Hoon Hong",
"Carl Wivagg",
"Kanna Shimizu"
] | https://www.isca-archive.org/interspeech_2023/huang23g_interspeech.html | https://www.isca-archive.org/interspeech_2023/huang23g_interspeech.pdf | 10.21437/Interspeech.2023-2147 | 376-380 | @inproceedings{huang23g_interspeech,
title = {{Record Deduplication for Entity Distribution Modeling in ASR Transcripts}},
author = {Tianyu Huang and Chung Hoon Hong and Carl Wivagg and Kanna Shimizu},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {376--380},
doi = {10.21437... | Voice digital assistants must keep up with trending search queries. We rely on a speech recognition model using contextual biasing with a rapidly updated set of entities, instead of frequent model retraining, to keep up with trends. There are several challenges with this approach: (1) the entity set must be frequently ... | 2306.06246 | title_snapshot |
agrawal23_interspeech | Learning When to Trust Which Teacher for Weakly Supervised ASR | [
"Aakriti Agrawal",
"Milind Rao",
"Anit Kumar Sahu",
"Gopinath Chennupati",
"Andreas Stolcke"
] | https://www.isca-archive.org/interspeech_2023/agrawal23_interspeech.html | https://www.isca-archive.org/interspeech_2023/agrawal23_interspeech.pdf | 10.21437/Interspeech.2023-2205 | 381-385 | @inproceedings{agrawal23_interspeech,
title = {{Learning When to Trust Which Teacher for Weakly Supervised ASR}},
author = {Aakriti Agrawal and Milind Rao and Anit Kumar Sahu and Gopinath Chennupati and Andreas Stolcke},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {381--385},
do... | Automatic speech recognition (ASR) training can utilize multiple experts as teacher models, each trained on a specific domain or accent. Teacher models may be opaque in nature since their architecture may be not be known or their training cadence is different from that of the student ASR model. Still, the student model... | 2306.12012 | title_snapshot |
huang23f_interspeech | Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer | [
"Lu Huang",
"Boyu Li",
"Jun Zhang",
"Lu Lu",
"Zejun Ma"
] | https://www.isca-archive.org/interspeech_2023/huang23f_interspeech.html | https://www.isca-archive.org/interspeech_2023/huang23f_interspeech.pdf | 10.21437/Interspeech.2023-1313 | 386-390 | @inproceedings{huang23f_interspeech,
title = {{Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer}},
author = {Lu Huang and Boyu Li and Jun Zhang and Lu Lu and Zejun Ma},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {386--390},
doi = {10.2143... | Domain adaptation using text-only corpus is challenging in end-to-end(E2E) speech recognition. Adaptation by synthesizing audio from text through TTS is resource-consuming. We present a method to learn Unified Speech-Text Representation in Conformer Transducer(USTR-CT) to enable fast domain adaptation using the text-on... | 2306.04076 | title_snapshot |
peng23e_interspeech | Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model | [
"Puyuan Peng",
"Shang-Wen Li",
"Okko Räsänen",
"Abdelrahman Mohamed",
"David Harwath"
] | https://www.isca-archive.org/interspeech_2023/peng23e_interspeech.html | https://www.isca-archive.org/interspeech_2023/peng23e_interspeech.pdf | 10.21437/Interspeech.2023-2044 | 391-395 | @inproceedings{peng23e_interspeech,
title = {{Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model}},
author = {Puyuan Peng and Shang-Wen Li and Okko Räsänen and Abdelrahman Mohamed and David Harwath},
year = {2023},
booktitle = {{Interspeech 202... | In this paper, we show that representations capturing syllabic units emerge when training a self-supervised speech model with a visually-grounded training objective. We demonstrate that a nearly identical model architecture (HuBErT) trained with a masked language modeling loss does not exhibit this same ability, sugges... | 2305.11435 | title_snapshot |
peng23d_interspeech | Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization | [
"Puyuan Peng",
"Brian Yan",
"Shinji Watanabe",
"David Harwath"
] | https://www.isca-archive.org/interspeech_2023/peng23d_interspeech.html | https://www.isca-archive.org/interspeech_2023/peng23d_interspeech.pdf | 10.21437/Interspeech.2023-2032 | 396-400 | @inproceedings{peng23d_interspeech,
title = {{Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization}},
author = {Puyuan Peng and Brian Yan and Shinji Watanabe and David Harwath},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {396--400},
doi ... | We investigate the emergent abilities of the recently proposed web-scale speech model Whisper, by adapting it to unseen tasks with prompt engineering. We selected three tasks: audio-visual speech recognition (AVSR), code-switched speech recognition (CS-ASR), and speech translation (ST) on unseen language pairs. we desi... | 2305.11095 | title_snapshot |
moore23_interspeech | Progress and Prospects for Spoken Language Technology: Results from Five Sexennial Surveys | [
"Roger K. Moore",
"Ricard Marxer"
] | https://www.isca-archive.org/interspeech_2023/moore23_interspeech.html | https://www.isca-archive.org/interspeech_2023/moore23_interspeech.pdf | 10.21437/Interspeech.2023-235 | 401-405 | @inproceedings{moore23_interspeech,
title = {{Progress and Prospects for Spoken Language Technology: Results from Five Sexennial Surveys}},
author = {Roger K. Moore and Ricard Marxer},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {401--405},
doi = {10.21437/Interspeech.2023... | Every six years (since 1997), a survey has been conducted at the IEEE workshop on Automatic Speech Recognition and Understanding (ASRU). The aim has been to gain an insight into the research community's perspective on the 'progress and prospects' for spoken language technology. These surveys have been based on a set of... | null | null |
sanabria23_interspeech | Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling | [
"Ramon Sanabria",
"OndÅej Klejch",
"Hao Tang",
"Sharon Goldwater"
] | https://www.isca-archive.org/interspeech_2023/sanabria23_interspeech.html | https://www.isca-archive.org/interspeech_2023/sanabria23_interspeech.pdf | 10.21437/Interspeech.2023-268 | 406-410 | @inproceedings{sanabria23_interspeech,
title = {{Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling}},
author = {Ramon Sanabria and OndÅej Klejch and Hao Tang and Sharon Goldwater},
year = {2023},
booktitle = {{Interspeech 2023}},
pages ... | Acoustic word embeddings are typically created by training a pooling function using pairs of word-like units. For unsupervised systems, these are mined using k-nearest neighbor (KNN) search, which is slow. Recently, mean-pooled representations from a pre-trained self-supervised English model were suggested as a promisi... | 2306.02153 | title_snapshot |
shi23d_interspeech | CASA-ASR: Context-Aware Speaker-Attributed ASR | [
"Mohan Shi",
"Zhihao Du",
"Qian Chen",
"Fan Yu",
"Yangze Li",
"Shiliang Zhang",
"Jie Zhang",
"Li-Rong Dai"
] | https://www.isca-archive.org/interspeech_2023/shi23d_interspeech.html | https://www.isca-archive.org/interspeech_2023/shi23d_interspeech.pdf | 10.21437/Interspeech.2023-601 | 411-415 | @inproceedings{shi23d_interspeech,
title = {{CASA-ASR: Context-Aware Speaker-Attributed ASR}},
author = {Mohan Shi and Zhihao Du and Qian Chen and Fan Yu and Yangze Li and Shiliang Zhang and Jie Zhang and Li-Rong Dai},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {411--415},
doi ... | Recently, speaker-attributed automatic speech recognition (SAASR) has attracted a wide attention, which aims at answering the question "who spoke what". Different from modular systems, end-to-end (E2E) SA-ASR minimizes the speaker-dependent recognition errors directly and shows a promising applicability. In this paper,... | 2305.12459 | title_snapshot |
takahashi23_interspeech | Unsupervised Learning of Discrete Latent Representations with Data-Adaptive Dimensionality from Continuous Speech Streams | [
"Shun Takahashi",
"Sakriani Sakti"
] | https://www.isca-archive.org/interspeech_2023/takahashi23_interspeech.html | https://www.isca-archive.org/interspeech_2023/takahashi23_interspeech.pdf | 10.21437/Interspeech.2023-1321 | 416-420 | @inproceedings{takahashi23_interspeech,
title = {{Unsupervised Learning of Discrete Latent Representations with Data-Adaptive Dimensionality from Continuous Speech Streams}},
author = {Shun Takahashi and Sakriani Sakti},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {416--420},
do... | This work presents a novel deep generative model for unsupervised learning of sparse binary feature representations with data-adaptive dimensionality directly from continuous speech streams. Sharing the critical assumption of unbounded latent dimensionality with previously proposed Bayesian non-parametric approaches, o... | null | null |
yang23n_interspeech | AD-TUNING: An Adaptive CHILD-TUNING Approach to Efficient Hyperparameter Optimization of Child Networks for Speech Processing Tasks in the SUPERB Benchmark | [
"Gaobin Yang",
"Jun Du",
"Maokui He",
"Shutong Niu",
"Baoxiang Li",
"Jiakui Li",
"Chin-Hui Lee"
] | https://www.isca-archive.org/interspeech_2023/yang23n_interspeech.html | https://www.isca-archive.org/interspeech_2023/yang23n_interspeech.pdf | 10.21437/Interspeech.2023-1167 | 421-425 | @inproceedings{yang23n_interspeech,
title = {{AD-TUNING: An Adaptive CHILD-TUNING Approach to Efficient Hyperparameter Optimization of Child Networks for Speech Processing Tasks in the SUPERB Benchmark}},
author = {Gaobin Yang and Jun Du and Maokui He and Shutong Niu and Baoxiang Li and Jiakui Li and Chin-Hu... | In this paper, we propose AD-TUNING, an adaptive CHILD-TUNING approach for hyperparameter tuning of child networks. To address the issue of selecting an optimal hyperparameter set P, which often varies for different tasks in CHILD-TUNING, we first analyze the distribution of parameter importance to ascertain the range ... | null | null |
wong23_interspeech | Distilling knowledge from Gaussian process teacher to neural network student | [
"Jeremy H. M. Wong",
"Huayun Zhang",
"Nancy F. Chen"
] | https://www.isca-archive.org/interspeech_2023/wong23_interspeech.html | https://www.isca-archive.org/interspeech_2023/wong23_interspeech.pdf | 10.21437/Interspeech.2023-190 | 426-430 | @inproceedings{wong23_interspeech,
title = {{Distilling knowledge from Gaussian process teacher to neural network student}},
author = {Jeremy H. M. Wong and Huayun Zhang and Nancy F. Chen},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {426--430},
doi = {10.21437/Interspeech... | Neural Networks (NN) and Gaussian Processes (GP) are different modelling approaches. The former stores characteristics of the training data in its many parameters, and then performs inference by parsing inputs through these parameters. The latter instead performs inference by computing a similarity between the test and... | null | null |
bhati23_interspeech | Segmental SpeechCLIP: Utilizing Pretrained Image-text Models for Audio-Visual Learning | [
"Saurabhchand Bhati",
"Jesús Villalba",
"Laureano Moro-Velazquez",
"Thomas Thebaud",
"Najim Dehak"
] | https://www.isca-archive.org/interspeech_2023/bhati23_interspeech.html | https://www.isca-archive.org/interspeech_2023/bhati23_interspeech.pdf | 10.21437/Interspeech.2023-135 | 431-435 | @inproceedings{bhati23_interspeech,
title = {{Segmental SpeechCLIP: Utilizing Pretrained Image-text Models for Audio-Visual Learning}},
author = {Saurabhchand Bhati and Jesús Villalba and Laureano Moro-Velazquez and Thomas Thebaud and Najim Dehak},
year = {2023},
booktitle = {{Interspeech 2023}},
... | Visually grounded models learn from paired images and their spoken captions. Recently, there have been attempts to utilize the visually grounded models trained from images and their corresponding text captions, such as CLIP, to improve speech-based visually grounded models' performance. However, the majority of these m... | null | null |
jacobs23_interspeech | Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili | [
"Christiaan Jacobs",
"Nathanaël Carraz Rakotonirina",
"Everlyn Asiko Chimoto",
"Bruce A. Bassett",
"Herman Kamper"
] | https://www.isca-archive.org/interspeech_2023/jacobs23_interspeech.html | https://www.isca-archive.org/interspeech_2023/jacobs23_interspeech.pdf | 10.21437/Interspeech.2023-421 | 436-440 | @inproceedings{jacobs23_interspeech,
title = {{Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili}},
author = {Christiaan Jacobs and Nathanaël Carraz Rakotonirina and Everlyn Asiko Chimoto and Bruce A. Bassett and Herman Kamper},
year ... | We consider hate speech detection through keyword spotting on radio broadcasts. One approach is to build an automatic speech recognition (ASR) system for the target low-resource language. We compare this to using acoustic word embedding (AWE) models that map speech segments to a space where matching words have similar ... | 2306.00410 | title_snapshot |
vandermerwe23_interspeech | Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-Learning | [
"Ruan van der Merwe",
"Herman Kamper"
] | https://www.isca-archive.org/interspeech_2023/vandermerwe23_interspeech.html | https://www.isca-archive.org/interspeech_2023/vandermerwe23_interspeech.pdf | 10.21437/Interspeech.2023-385 | 441-445 | @inproceedings{vandermerwe23_interspeech,
title = {{Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-Learning}},
author = {Ruan {van der Merwe} and Herman Kamper},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {441--445},
doi = {10.2143... | We consider the problem of few-shot spoken word classification in a setting where a model is incrementally introduced to new word classes. This would occur in a user-defined keyword system where new words can be added as the system is used. In such a continual learning scenario, a model might start to misclassify earli... | 2305.13080 | title_snapshot |
polacek23_interspeech | Online Punctuation Restoration using ELECTRA Model for streaming ASR Systems | [
"Martin PoláÄek",
"Petr Äerva",
"JindÅich ŽÄánský",
"Lenka Weingartová"
] | https://www.isca-archive.org/interspeech_2023/polacek23_interspeech.html | https://www.isca-archive.org/interspeech_2023/polacek23_interspeech.pdf | 10.21437/Interspeech.2023-664 | 446-450 | @inproceedings{polacek23_interspeech,
title = {{Online Punctuation Restoration using ELECTRA Model for streaming ASR Systems}},
author = {Martin PoláÄek and Petr Äerva and JindÅich ŽÄánský and Lenka Weingartová},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {446--450},
d... | In this work, we propose a lightweight online approach to automatic punctuation restoration (APR), which can be utilized in speech transcription systems for, e.g., live captioning TV or radio streams. It uses only text input without prosodic features and a fine-tuned ELECTRA-Small model with a two-layer classification ... | null | null |
chen23s_interspeech | Language Agnostic Data-Driven Inverse Text Normalization | [
"Szu-Jui Chen",
"Debjyoti Paul",
"Yutong Pang",
"Peng Su",
"Xuedong Zhang"
] | https://www.isca-archive.org/interspeech_2023/chen23s_interspeech.html | https://www.isca-archive.org/interspeech_2023/chen23s_interspeech.pdf | 10.21437/Interspeech.2023-2066 | 451-455 | @inproceedings{chen23s_interspeech,
title = {{Language Agnostic Data-Driven Inverse Text Normalization}},
author = {Szu-Jui Chen and Debjyoti Paul and Yutong Pang and Peng Su and Xuedong Zhang},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {451--455},
doi = {10.21437/Inters... | The rise of automatic speech recognition (ASR) models has created an urgent need for converting spoken language into written text to provide better user experiences. This has drawn the attention of researchers, particularly for real-time on-device ASR deployment, towards the inverse text normalization (ITN) problem. Wh... | 2301.08506 | title_snapshot |
chen23j_interspeech | How to Estimate Model Transferability of Pre-Trained Speech Models? | [
"Zih-Ching Chen",
"Chao-Han Huck Yang",
"Bo Li",
"Yu Zhang",
"Nanxin Chen",
"Shuo-Yiin Chang",
"Rohit Prabhavalkar",
"Hung-yi Lee",
"Tara Sainath"
] | https://www.isca-archive.org/interspeech_2023/chen23j_interspeech.html | https://www.isca-archive.org/interspeech_2023/chen23j_interspeech.pdf | 10.21437/Interspeech.2023-1079 | 456-460 | @inproceedings{chen23j_interspeech,
title = {{How to Estimate Model Transferability of Pre-Trained Speech Models?}},
author = {Zih-Ching Chen and Chao-Han Huck Yang and Bo Li and Yu Zhang and Nanxin Chen and Shuo-Yiin Chang and Rohit Prabhavalkar and Hung-yi Lee and Tara Sainath},
year = {2023},
boo... | In this work, we introduce a ''score-based assessment'' framework for estimating the transferability of pre-trained speech models (PSMs) for fine-tuning target tasks. We leverage upon two representation theories, Bayesian likelihood estimation and optimal transport, to generate rank scores for the PSM candidates using ... | 2306.01015 | title_snapshot |
ihori23_interspeech | Transcribing Speech as Spoken and Written Dual Text Using an Autoregressive Model | [
"Mana Ihori",
"Hiroshi Sato",
"Tomohiro Tanaka",
"Ryo Masumura",
"Saki Mizuno",
"Nobukatsu Hojo"
] | https://www.isca-archive.org/interspeech_2023/ihori23_interspeech.html | https://www.isca-archive.org/interspeech_2023/ihori23_interspeech.pdf | 10.21437/Interspeech.2023-1655 | 461-465 | @inproceedings{ihori23_interspeech,
title = {{Transcribing Speech as Spoken and Written Dual Text Using an Autoregressive Model}},
author = {Mana Ihori and Hiroshi Sato and Tomohiro Tanaka and Ryo Masumura and Saki Mizuno and Nobukatsu Hojo},
year = {2023},
booktitle = {{Interspeech 2023}},
pages ... | This paper proposes a novel method to jointly generate spoken and written text from input speech for expanding use cases of speech-based applications. The spoken text generated using speech-to-spoken text systems, i.e., speech recognition systems, has disfluencies and no punctuation marks. Thus, spoken text is often co... | null | null |
yuksel23_interspeech | NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive Learning | [
"Kamer Ali Yuksel",
"Thiago Castro Ferreira",
"Golara Javadi",
"Mohamed Al-Badrashiny",
"Ahmet Gunduz"
] | https://www.isca-archive.org/interspeech_2023/yuksel23_interspeech.html | https://www.isca-archive.org/interspeech_2023/yuksel23_interspeech.pdf | 10.21437/Interspeech.2023-643 | 466-470 | @inproceedings{yuksel23_interspeech,
title = {{NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive Learning}},
author = {Kamer Ali Yuksel and Thiago Castro Ferreira and Golara Javadi and Mohamed Al-Badrashiny and Ahmet Gundu... | This paper introduces NoRefER, a novel referenceless quality metric for automatic speech recognition (ASR) systems. Traditional reference-based metrics for evaluating ASR systems require costly ground-truth transcripts. NoRefER overcomes this limitation by fine-tuning a multilingual language model for pair-wise ranking... | 2306.12577 | title_snapshot |
gu23c_interspeech | Scaling Laws for Discriminative Speech Recognition Rescoring Models | [
"Yile Gu",
"Prashanth Gurunath Shivakumar",
"Jari Kolehmainen",
"Ankur Gandhe",
"Ariya Rastrow",
"Ivan Bulyko"
] | https://www.isca-archive.org/interspeech_2023/gu23c_interspeech.html | https://www.isca-archive.org/interspeech_2023/gu23c_interspeech.pdf | 10.21437/Interspeech.2023-2128 | 471-475 | @inproceedings{gu23c_interspeech,
title = {{Scaling Laws for Discriminative Speech Recognition Rescoring Models}},
author = {Yile Gu and Prashanth {Gurunath Shivakumar} and Jari Kolehmainen and Ankur Gandhe and Ariya Rastrow and Ivan Bulyko},
year = {2023},
booktitle = {{Interspeech 2023}},
pages ... | Recent studies have found that model performance has a smooth power-law relationship, or scaling laws, with training data and model size, for a wide range of problems. These scaling laws allow one to choose nearly optimal data and model sizes. We study whether this scaling property is also applicable to second-pass res... | 2306.15815 | title_snapshot |
liu23w_interspeech | Exploring Energy-based Language Models with Different Architectures and Training Methods for Speech Recognition | [
"Hong Liu",
"Zhaobiao Lv",
"Zhijian Ou",
"Wenbo Zhao",
"Qing Xiao"
] | https://www.isca-archive.org/interspeech_2023/liu23w_interspeech.html | https://www.isca-archive.org/interspeech_2023/liu23w_interspeech.pdf | 10.21437/Interspeech.2023-2429 | 476-480 | @inproceedings{liu23w_interspeech,
title = {{Exploring Energy-based Language Models with Different Architectures and Training Methods for Speech Recognition}},
author = {Hong Liu and Zhaobiao Lv and Zhijian Ou and Wenbo Zhao and Qing Xiao},
year = {2023},
booktitle = {{Interspeech 2023}},
pages ... | Energy-based language models (ELMs) parameterize an unnormalized distribution for natural sentences and are radically different from popular autoregressive language models (ALMs). As an important application, ELMs have been successfully used as a means for calculating sentence scores in speech recognition, but they all... | 2305.12676 | title_snapshot |
feng23d_interspeech | Memory Augmented Lookup Dictionary Based Language Modeling for Automatic Speech Recognition | [
"Yukun Feng",
"Ming Tu",
"Rui Xia",
"Chuanzeng Huang",
"Yuxuan Wang"
] | https://www.isca-archive.org/interspeech_2023/feng23d_interspeech.html | https://www.isca-archive.org/interspeech_2023/feng23d_interspeech.pdf | 10.21437/Interspeech.2023-1362 | 481-485 | @inproceedings{feng23d_interspeech,
title = {{Memory Augmented Lookup Dictionary Based Language Modeling for Automatic Speech Recognition}},
author = {Yukun Feng and Ming Tu and Rui Xia and Chuanzeng Huang and Yuxuan Wang},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {481--485},
... | Recent studies have shown that using an external Language Model (LM) benefits the end-to-end Automatic Speech Recognition (ASR). However, predicting tokens that appear less frequently in the training set is still quite challenging. The long-tail prediction problems have been widely studied in many applications, but onl... | 2301.00066 | title_snapshot |
iwamoto23_interspeech | Memory Network-Based End-To-End Neural ES-KMeans for Improved Word Segmentation | [
"Yu Iwamoto",
"Takahiro Shinozaki"
] | https://www.isca-archive.org/interspeech_2023/iwamoto23_interspeech.html | https://www.isca-archive.org/interspeech_2023/iwamoto23_interspeech.pdf | 10.21437/Interspeech.2023-1251 | 486-490 | @inproceedings{iwamoto23_interspeech,
title = {{Memory Network-Based End-To-End Neural ES-KMeans for Improved Word Segmentation}},
author = {Yu Iwamoto and Takahiro Shinozaki},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {486--490},
doi = {10.21437/Interspeech.2023-1251},
... | Unsupervised word learning from unlabeled speech is a fundamental problem in zero-resource speech processing, which enables dialogue agents to learn new words directly from spoken utterances. The embedded segmental K-means (ES-KMeans) is a representative unsupervised word segmentation method. However, it has a heteroge... | null | null |
sudo23b_interspeech | Retraining-free Customized ASR for Enharmonic Words Based on a Named-Entity-Aware Model and Phoneme Similarity Estimation | [
"Yui Sudo",
"Kazuya Hata",
"Kazuhiro Nakadai"
] | https://www.isca-archive.org/interspeech_2023/sudo23b_interspeech.html | https://www.isca-archive.org/interspeech_2023/sudo23b_interspeech.pdf | 10.21437/Interspeech.2023-1320 | 491-495 | @inproceedings{sudo23b_interspeech,
title = {{Retraining-free Customized ASR for Enharmonic Words Based on a Named-Entity-Aware Model and Phoneme Similarity Estimation}},
author = {Yui Sudo and Kazuya Hata and Kazuhiro Nakadai},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {491--49... | End-to-end automatic speech recognition (E2E-ASR) has the potential to improve performance, but a specific issue that needs to be addressed is the difficulty it has in handling enharmonic words: named entities (NEs) with the same pronunciation and part of speech that are spelled differently. This often occurs with Japa... | 2305.17846 | title_snapshot |
zhu23c_interspeech | Lightweight and Efficient Spoken Language Identification of Long-form Audio | [
"Winstead Zhu",
"Md Iftekhar Tanveer",
"Yang Janet Liu",
"Seye Ojumu",
"Rosie Jones"
] | https://www.isca-archive.org/interspeech_2023/zhu23c_interspeech.html | https://www.isca-archive.org/interspeech_2023/zhu23c_interspeech.pdf | 10.21437/Interspeech.2023-304 | 496-500 | @inproceedings{zhu23c_interspeech,
title = {{Lightweight and Efficient Spoken Language Identification of Long-form Audio}},
author = {Winstead Zhu and Md Iftekhar Tanveer and Yang Janet Liu and Seye Ojumu and Rosie Jones},
year = {2023},
booktitle = {{Interspeech 2023}},
pages = {496--500},
... | State-of-the-art Spoken Language Identification (SLI) systems usually focus on tackling short audio clips, and thus their performance degrade drastically when applied to long-form audio, such as podcast, which poses peculiar challenges to existing SLI approaches due to its long duration and diverse content that frequen... | null | null |
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