Preparation of Bangla Speech Corpus from Publicly Available Audio & Text (2020.lrec-1)
Copied to clipboard
Shafayat Ahmed, Nafis Sadeq, Sudipta Saha Shubha, Md. Nahidul Islam, Muhammad Abdullah Adnan, Mohammad Zuberul Islam
| Challenge: | Automated speech recognition systems require large annotated speech corpus for training. |
| Approach: | They propose to use publicly available Bangla audiobooks and TV news recordings as input to prepare a large speech corpus with reasonable confidence. |
| Outcome: | The proposed algorithm outperforms the existing speech corpus and the existing corpus with speaker diarization and gender detection. |
Similar Papers
Improving End-to-End Bangla Speech Recognition with Semi-supervised Training (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to train speech recognition systems require large annotated corpus. |
| Approach: | They propose a semi-supervised training approach that exploits large unpaired audio and text data to improve the performance of an automatic speech recognition system. |
| Outcome: | The proposed method reduces the WER of the system from 37% to 31.9%. |
Gold Standard Bangla OCR Dataset: An In-Depth Look at Data Preprocessing and Annotation Processes (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Existing datasets designed specifically for the Bengali language have been limited. |
| Approach: | They propose to use a large collection of labeled Bangla text image datasets to improve the performance of Bangla OCR. |
| Outcome: | The proposed system is the most extensive gold standard corpus for Bangla characters and words, comprising over 4 million human-annotated images. |
A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline (2021.eacl-main)
Copied to clipboard
Yerbolat Khassanov, Saida Mussakhojayeva, Almas Mirzakhmetov, Alen Adiyev, Mukhamet Nurpeiissov, Huseyin Atakan Varol
| Challenge: | The Kazakh speech corpus contains over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. |
| Approach: | They propose to build an open-source Kazakh speech corpus for the Kazakh language that contains over 153,000 transcribed audio . they describe the data collection and preprocessing procedures followed by a description of the database specifications. |
| Outcome: | The Kazakh speech corpus contains over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. |
Developing the Bangla RST Discourse Treebank (L18-1)
Copied to clipboard
| Challenge: | a corpus in Bangla is annotated for coherence relations between text segments representing propositions . the corpus is a valuable resource for conducting discourse studies for Bangla . |
| Approach: | They propose to build a Bangla-annotated corpus which includes 266 Bangla texts . they use Rhetorical Structure Theory as the theoretical framework to develop the corpus . |
| Outcome: | The proposed corpus contains 266 Bangla texts annotated for coherence relations . the research could be used for discourse studies and for developing NLP applications . |
SHONGLAP: A Large Bengali Open-Domain Dialogue Corpus (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing open-domain dialogue systems suffer from data scarcity due to unavailability of high-quality datasets for low-resource languages like Bengali. |
| Approach: | They propose to prepare large-scale open-domain dialogue datasets from podcasts and talk-shows and label them based on weak-supervision techniques. |
| Outcome: | The proposed corpus improves performance of large language models in case of downstream classification tasks during fine-tuning. |
BanglaByT5: Byte-Level Modelling for Bangla (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have achievedremarkable success across various natural lan-guage processing tasks. |
| Approach: | They propose a byte-level encoder-decoder model specifically tailored for Bangla. |
| Outcome: | The proposed model outperforms existing models in gen-erative and classification tasks and surpasses several multilingual and larger models. |
The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing work in the area of radio browsing using automatic speech recognition (ASR) has been done by the United Nations in Uganda, and Keyword Spotting systems in Somalia. |
| Approach: | They propose to use a Luganda radio speech corpus of 155 hours to build a usable radio monitoring automatic speech recognition system. |
| Outcome: | The makerere artificial intelligence lab releases a Luganda radio speech corpus of 155 hours. |
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus (2021.acl-long)
Copied to clipboard
| Challenge: | QASR is the largest transcribed Arabic speech corpus in the broadcast domain. |
| Approach: | They introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. |
| Outcome: | The proposed dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news channel. |
RSC: A Romanian Read Speech Corpus for Automatic Speech Recognition (2020.lrec-1)
Copied to clipboard
| Challenge: | Romanian language is under-resourced due to the lack of acoustic and linguistic resources. |
| Approach: | They propose to use a Romanian speech corpus to train automatic speech recognition algorithms based on the spoken hotword detection mechanism. |
| Outcome: | The read speech corpus is a speech recognition system that can perform automatic speech recognition and speech synthesis using state-of-the-art speech recognition toolkit. |
BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering (2024.lrec-main)
Copied to clipboard
| Challenge: | Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. |
| Approach: | Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. authors propose a framework that can automatically construct Bengali KG from any Bangla text. |
| Outcome: | The proposed framework can automatically construct Bengali KGs from any Bangla text. |