Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin

• URL: http://arxiv.org/abs/2010.11123v1
• Date: Wed, 21 Oct 2020 16:32:58 GMT
• Title: Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin
• Authors: Daniel Ajisafe, Oluwabukola Adegboro, Esther Oduntan, Tayo Arulogun
• Abstract summary: Nigerian pidgin is one of the most popular languages in West Africa. We present the first parallel (speech-to-text) data on Nigerian pidgin. We also trained the first end-to-end speech recognition system on this language.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nigerian Pidgin remains one of the most popular languages in West Africa. With at least 75 million speakers along the West African coast, the language has spread to diasporic communities through Nigerian immigrants in England, Canada, and America, amongst others. In contrast, the language remains an under-resourced one in the field of natural language processing, particularly on speech recognition and translation tasks. In this work, we present the first parallel (speech-to-text) data on Nigerian pidgin. We also trained the first end-to-end speech recognition system (QuartzNet and Jasper model) on this language which were both optimized using Connectionist Temporal Classification (CTC) loss. With baseline results, we were able to achieve a low word error rate (WER) of 0.77% using a greedy decoder on our dataset. Finally, we open-source the data and code along with this publication in order to encourage future research in this direction.

Related papers

• Supporting SENCOTEN Language Documentation Efforts with Automatic Speech Recognition [4.702636570667311]
  The SENCOTEN language, spoken on the Saanich peninsula of southern Vancouver Island, is in the midst of vigorous language revitalization efforts.<n>We propose an ASR-driven documentation pipeline that leverages augmented speech data from a text-to-speech system.<n>An n-gram language model is also incorporated via shallow fusion or n-best restoring to maximize the use of available data.
  arXiv  Detail & Related papers  (2025-07-14T21:44:35Z)
• Voices Unheard: NLP Resources and Models for Yorùbá Regional Dialects [72.18753241750964]
  Yorub'a is an African language with roughly 47 million speakers. Recent efforts to develop NLP technologies for African languages have focused on their standard dialects. We take steps towards bridging this gap by introducing a new high-quality parallel text and speech corpus.
  arXiv  Detail & Related papers  (2024-06-27T22:38:04Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• A Novel Self-training Approach for Low-resource Speech Recognition [15.612232220719653]
  We propose a self-training approach for automatic speech recognition (ASR) for low-resource settings. Our approach significantly improves word error rate, achieving a relative improvement of 14.94%. Our proposed approach reports the best results on the Common Voice Punjabi dataset.
  arXiv  Detail & Related papers  (2023-08-10T01:02:45Z)
• Strategies for improving low resource speech to text translation relying on pre-trained ASR models [59.90106959717875]
  This paper presents techniques and findings for improving the performance of low-resource speech to text translation (ST) We conducted experiments on both simulated and real-low resource setups, on language pairs English - Portuguese, and Tamasheq - French respectively.
  arXiv  Detail & Related papers  (2023-05-31T21:58:07Z)
• MasakhaNEWS: News Topic Classification for African languages [15.487928928173098]
  African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. We develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa.
  arXiv  Detail & Related papers  (2023-04-19T21:12:23Z)
• AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages [45.88640066767242]
  Africa is home to over 2,000 languages from more than six language families and has the highest linguistic diversity among all continents. Yet, there is little NLP research conducted on African languages. Crucial to enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, a sentiment analysis benchmark that contains a total of >110,000 tweets in 14 African languages.
  arXiv  Detail & Related papers  (2023-02-17T15:40:12Z)
• From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognition [50.93943755401025]
  We propose a new parameter-efficient learning framework based on neural model reprogramming for cross-lingual speech recognition. We design different auxiliary neural architectures focusing on learnable pre-trained feature enhancement. Our methods outperform existing ASR tuning architectures and their extension with self-supervised losses.
  arXiv  Detail & Related papers  (2023-01-19T02:37:56Z)
• MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition [55.95128479289923]
  African languages are spoken by over a billion people, but are underrepresented in NLP research and development. We create the largest human-annotated NER dataset for 20 African languages. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points.
  arXiv  Detail & Related papers  (2022-10-22T08:53:14Z)
• No Language Left Behind: Scaling Human-Centered Machine Translation [69.28110770760506]
  We create datasets and models aimed at narrowing the performance gap between low and high-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2022-07-11T07:33:36Z)
• \`It\`ak\'ur\`oso: Exploiting Cross-Lingual Transferability for Natural Language Generation of Dialogues in Low-Resource, African Languages [0.9511471519043974]
  We investigate the possibility of cross-lingual transfer from a state-of-the-art (SoTA) deep monolingual model to 6 African languages. The languages are Swahili, Wolof, Hausa, Nigerian Pidgin English, Kinyarwanda & Yorub'a. The results show that the hypothesis that deep monolingual models learn some abstractions that generalise across languages holds.
  arXiv  Detail & Related papers  (2022-04-17T20:23:04Z)
• Speaker-Conditioned Hierarchical Modeling for Automated Speech Scoring [60.55025339250815]
  We propose a novel deep learning technique for non-native ASS, called speaker-conditioned hierarchical modeling. We take advantage of the fact that oral proficiency tests rate multiple responses for a candidate. In our technique, we take advantage of the fact that oral proficiency tests rate multiple responses for a candidate. We extract context from these responses and feed them as additional speaker-specific context to our network to score a particular response.
  arXiv  Detail & Related papers  (2021-08-30T07:00:28Z)
• Using Radio Archives for Low-Resource Speech Recognition: Towards an Intelligent Virtual Assistant for Illiterate Users [3.3946853660795884]
  In many countries, illiterate people tend to speak only low-resource languages. We investigate the effectiveness of unsupervised speech representation learning on noisy radio broadcasting archives. Our contributions offer a path forward for ethical AI research to serve the needs of those most disadvantaged by the digital divide.
  arXiv  Detail & Related papers  (2021-04-27T10:09:34Z)
• Leveraging neural representations for facilitating access to untranscribed speech from endangered languages [10.61744395262441]
  We use data selected from 7 Australian Aboriginal languages and a regional variety of Dutch. We find that representations from the middle layers of the wav2vec 2.0 Transformer offer large gains in task performance. While features extracted using the pre-trained English model yielded improved detection on all the evaluation languages, better detection performance was associated with the evaluation language's phonological similarity to English.
  arXiv  Detail & Related papers  (2021-03-26T16:44:08Z)
• Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddings [72.69253034282035]
  We exploit a language independent multilingual sentence representation to easily generalize to a new language. Blindly decoding from Portuguese using a basesystem containing several Romance languages we achieve scores of 36.4 BLEU for Portuguese-English and 12.8 BLEU for Russian-English. We explore a more practical adaptation approach through non-iterative backtranslation, exploiting our model's ability to produce high quality translations.
  arXiv  Detail & Related papers  (2021-03-11T14:22:08Z)
• Transfer Learning based Speech Affect Recognition in Urdu [0.0]
  We pre-train a model for high resource language affect recognition task and fine tune the parameters for low resource language. This approach achieves high Unweighted Average Recall (UAR) when compared with existing algorithms.
  arXiv  Detail & Related papers  (2021-03-05T10:30:58Z)
• Igbo-English Machine Translation: An Evaluation Benchmark [3.0151383439513753]
  We discuss our effort toward building a standard machine translation benchmark dataset for Igbo. Igbo is spoken by more than 50 million people globally with over 50% of the speakers are in southeastern Nigeria.
  arXiv  Detail & Related papers  (2020-04-01T18:06:21Z)
• Towards Neural Machine Translation for Edoid Languages [2.144787054581292]
  Many Nigerian languages have relinquished their previous prestige and purpose in modern society to English and Nigerian Pidgin. This work explores the feasibility of Neural Machine Translation for the Edoid language family of Southern Nigeria.
  arXiv  Detail & Related papers  (2020-03-24T07:53:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.