Objective
All current successful automatic speech recognition (ASR) systems (largely for English) are trained on hundreds of thousands of hours of training data, invariably based on individual languages. However, multilingual communities switch between two or more languages (code-switching): E What is my bank balance? Bengali/E amar bank balance kɔto? (my bank balance how much). Thus, for the recognition of speech involving code-switching, recognition rates are seriously impaired. Moreover, since code-switching across more than one language can easily violate standard Large Language Models (LLMs), this constitutes a serious setback for recognition. Recently, end-to-end systems have been used to model multilingual speech including code-switching. However, their performance is impeded by the scarcity of massive amounts of good quality code-switching speech and text training data. Errors in these transcriptions can have dangerous impacts when used in AI analytics pathways.
We have successfully built a single-word recognition system (FlexSR) based on linguistic principles (phonological features). We have shown that FlexSR can be adapted across Germanic languages and different accents by altering the lexical representation of words without additional training. It has proven to be accurate, fast, and computationally lightweight for word recognition in Dutch, English and German. The FlexSR system, including the word corpora, is smaller than 10MB. Since the extraction of phonological features makes FlexSR a powerful tool to identify words across languages, we will use additional linguistic information from our current ERC Synergy project PAAL to adapt the current FlexSR. We will extend our mispronunciation detection-based transcription verification system and build a short-phrase code-switching command/query system for banking or multimedia situations. We will focus on Bengali, Hindi and English with code-switching and accent variation across the three.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
You need to log in or register to use this function
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2026-POC
See all projects funded under this callHost institution
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
OX1 2JD Oxford
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.