
Summary
Code-switching is the use of multiple languages in the same utterance and is common in multilingual communities across the world. Code-switching poses many challenges to speech and NLP systems and has gained widespread interest in academia and industry recently. We organized special sessions on code-switching at Interspeech 2017, 2018 and 2019. In 2020, we will be organizing this as a virtual workshop immediately after Interspeech 2020.
We welcome papers related to, but not restricted to the following aspects of code-switching:
- Code-switched speech recognition and synthesis
- Language Modeling for code-switching
- Multilingual models for code-switching
- Data and resources for code-switching
- Code-switched chatbots and dialogue systems
- Code-switched speech analytics
*NEW* You can find the proceedings of the workshop here and view all pre-recorded talks in the Schedule tab.
*NEW* The workshop will be conducted on Microsoft Teams. All registered participants have been sent information about this by email.
Workshop timeline:
Shared task testing period: April 27-29 2020
First Paper submission deadline: June 5 2020
Paper acceptance notification: July 20 2020
1 page Abstract submission deadline for special track: Aug 9 2020
Abstract and paper acceptance notification (special track and second round): Sept 7 2020
Camera ready papers due (*Both Rounds*): Sept 20th 2020
Video submission deadline for accepted papers: 9th October 2020
Registration deadline: 15th October 2020
Workshop: 30 and 31 October 2020
Contact us:
Please write to sunayana.sitaram@microsoft.com
Schedule
Please note: This is a tentative schedule and is subject to change. All times are in China Standard Time (UTC+8).
Day 1 | Friday, 30 October 2020 | |||
Time (CST) | Session | Session Chairs | Title | Speaker |
20:30-21:30 | Opening remarks and Keynote | Points of connection between linguistics and speech technology with regard to code-switching | Barbara Bullock, Jacqueline Toribio | |
21:30-21:40 | Break | |||
21:40-21:55 | PaperS1 | Thamar Solorio and Manuel Mager | A Study of Types and Characteristics of Code-Switching in Mandarin-English Speech | Leijing Hou |
21:55-22:10 | PaperS1 | Thamar Solorio and Manuel Mager | Malayalam-English Code-Switched: Speech Corpus Development and Analysis | Sreeram Manghat |
22:10-22:25 | PaperS1 | Thamar Solorio and Manuel Mager | Understanding forced alignment errors in Hindi-English code-mixed speech — a feature analysis | Ayushi Pandey |
22:25-22:40 | PaperS1Q&A | Thamar Solorio and Manuel Mager | Q&A | |
22:40-22:50 | Break | |||
22:50-23:00 | SponsorTalk | Microsoft | Basil Abraham | |
23:00-23:15 | PaperS2 | Kalika Bali and Khyathi Chandu | Mere account mein kitna balance hai? – On building voice enabled Banking Services for Multilingual Communities | Akshat Gupta |
23:15-23:30 | PaperS2 | Kalika Bali and Khyathi Chandu | Investigating Modelling Techniques for Natural Language Inference on Code-Switched Dialogues in Bollywood Movies | Anjana Umapathy |
23:30-23:40 | PaperS2Q&A | Kalika Bali and Khyathi Chandu | Q&A | |
Day 2: | Saturday, 31 October 2020 | |||
Time (CST) | Session | Session Chairs | Title | |
20:30-20:45 | SharedTask | Sunayana Sitaram and Gustavo Aguilar | Opening Remarks and description of shared task | Sunayana Sitaram |
20:45-20:55 | SharedTask | Sunayana Sitaram and Gustavo Aguilar | Vocapia-LIMSI System for 2020 Shared Task on Code-switched Spoken Language Identification | Claude Barras |
20:55-21:05 | SharedTask | Sunayana Sitaram and Gustavo Aguilar | Exploiting Spectral Augmentation for Code-Switched Spoken Language Identification | Pradeep R |
21:05-21:15 | SharedTask | Sunayana Sitaram and Gustavo Aguilar | On detecting code mixing in speech using Discrete latent representations | Sai Krishna Rallabandi |
21:15-21:25 | SharedTask | Sunayana Sitaram and Gustavo Aguilar | Language Identification for Code-Mixed Indian Languages In The Wild | Parav Nagarsheth |
21:25-21:35 | SharedTask | Sunayana Sitaram and Gustavo Aguilar | Utterance-level Code-Switching Identification using Transformer Network | Krishna DN |
21:35-21:45 | SharedTaskQ&A | Sunayana Sitaram and Gustavo Aguilar | Q&A | |
21:45-22:00 | Break | |||
22:00-22:10 | SponsorTalk | SpeechOcean | Yufeng Hao | |
22:10-22:25 | PaperS3 | Genta Indra Winata and Sai Krishna Rallabandi | Learning not to Discriminate: Task Agnostic Learning for Improving Monolingual and Code-switched Speech Recognition | Sanket Shah |
22:25-22:40 | PaperS3 | Genta Indra Winata and Sai Krishna Rallabandi | Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages | Trideba Padhi |
22:40-22:55 | PaperS3 | Genta Indra Winata and Sai Krishna Rallabandi | The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results | Xian Shi |
22:55-23:10 | PaperS3Q&A | Genta Indra Winata and Sai Krishna Rallabandi | Q&A | |
23:10-23:20 | Closing remarks |
Keynote
Title: Points of connection between linguistics and speech technology with regard to code-switching
The study of multilingualism presents a unique challenge within the discipline of linguistics since, without exception, the major linguistic theories have been developed from a monolingual orientation. However, no language is completely insulated from all others; there is invariably some evidence of language contact in every grammar. In the speech of multilinguals, these effects can be significant. In this talk, we focus on the overt forms of language contact, as manifested by the phenomena of borrowing and code-switching. We will also touch on the covert form of contact, what we call convergence. Our aim is threefold: (i) to provide a comprehensive overview of the syntactic, lexical, phonetic, and pragmatic effects of borrowing, code-switching, and convergence; (iii) to examine the theories that attempt to account for linguistic patterns of codeswitching and borrowing; and (iii) to highlight points of connection to speech technologies.
Barbara E. Bullock (Ph.D., Linguistics, University of Delaware 1991) is Professor of Linguistics in the Department of French & Italian at the University of Texas. She specializes in the effects of bilingualism and language contact on linguistic structure, particularly on the phonetic systems. Her research projects investigate sociophonetics, code-switching and borrowing, language variation and change, and computational approaches to multilingualism. With colleagues and students, she has begun to explore the power of corpus linguistics and NLP as effective tools in research on bilingual speech forms working to quantify and visualize language mixing and its intermittency to enable cross-corpus comparisons and linguistic generalizations.
Almeida Jacqueline Toribio (Ph.D., Linguistics, Cornell University 1993) is Professor of Linguistics in the Department of Spanish and Portuguese at the University of Texas. Her research in formal linguistics investigates patterns of morphological and syntactic variation across languages and dialects as well as structural patterns of language mixing in bilingual code-switching; her complementary work in sociolinguistics considers the ways in which variables such as ethnicity, race, gender, literacy, and national origin are encoded through linguistic features and language choices. Her investigations employ diverse methods, from experimental elicitation, to ethnographies of rural and urban communities, to computational analyses of literary texts and popular media.
Leaderboard
Task A | ||||||||||
Gujarati | Telugu | Tamil | ||||||||
Team Name | Accuracy | EER | Team Name | Accuracy | EER | Team Name | Accuracy | EER | ||
VocapiaLIMSI | 0.75 | 0.12 | VocapiaLIMSI | 0.79 | 0.10 | VocapiaLIMSI | 0.79 | 0.10 | ||
Swiggy | 0.70 | 0.15 | Swiggy | 0.79 | 0.10 | Swiggy | 0.79 | 0.10 | ||
Ground Zero | 0.55 | 0.22 | CMU | 0.74 | 0.13 | CMU | 0.73 | 0.13 | ||
CMU | 0.50 | 0.25 | Sizzle | 0.71 | 0.14 | Ground Zero | 0.67 | 0.16 | ||
Sizzle | 0.47 | 0.26 | Ground Zero | 0.67 | 0.16 | Sizzle | 0.55 | 0.22 | ||
Task B |
||||||||||
Gujarati | Telugu | Tamil | ||||||||
Team Name | Accuracy | EER | Team Name | Accuracy | EER | Team Name | Accuracy | EER | ||
VocapiaLIMSI | 0.78 | 0.06 | VocapiaLIMSI | 0.79 | 0.06 | VocapiaLIMSI | 0.78 | 0.06 | ||
Swiggy | 0.75 | 0.07 | Swiggy | 0.74 | 0.07 | Swiggy | 0.74 | 0.07 |