Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach

• URL: http://arxiv.org/abs/2406.01446v1
• Date: Mon, 3 Jun 2024 15:38:40 GMT
• Title: Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach
• Authors: Ara Yeroyan, Nikolay Karpov,
• Abstract summary: This study introduces a novel pipeline designed to generate ASR training datasets from audiobooks. The common structure of these audiobooks poses a unique challenge due to the extensive length of audio segments. We propose a method for effectively aligning audio with its corresponding text and segmenting it into lengths suitable for ASR training.
• Score: 0.6445605125467574
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In recent years, automatic speech recognition (ASR) systems have significantly improved, especially in languages with a vast amount of transcribed speech data. However, ASR systems tend to perform poorly for low-resource languages with fewer resources, such as minority and regional languages. This study introduces a novel pipeline designed to generate ASR training datasets from audiobooks, which typically feature a single transcript associated with hours-long audios. The common structure of these audiobooks poses a unique challenge due to the extensive length of audio segments, whereas optimal ASR training requires segments ranging from 4 to 15 seconds. To address this, we propose a method for effectively aligning audio with its corresponding text and segmenting it into lengths suitable for ASR training. Our approach simplifies data preparation for ASR systems in low-resource languages and demonstrates its application through a case study involving the Armenian language. Our method, which is "portable" to many low-resource languages, not only mitigates the issue of data scarcity but also enhances the performance of ASR models for underrepresented languages.

Related papers

• Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection [49.27067541740956]
  Speech Emotion Recognition (SER) is a crucial component in developing general-purpose AI agents capable of natural human-computer interaction. Building robust multilingual SER systems remains challenging due to the scarcity of labeled data in languages other than English and Chinese. We propose an approach to enhance SER performance in low SER resource languages by leveraging data from high-resource languages.
  arXiv  Detail & Related papers  (2024-09-17T08:36:45Z)
• Meta-Whisper: Speech-Based Meta-ICL for ASR on Low-Resource Languages [51.12146889808824]
  Meta-Whisper is a novel approach to improve automatic speech recognition for low-resource languages. It enhances Whisper's ability to recognize speech in unfamiliar languages without extensive fine-tuning.
  arXiv  Detail & Related papers  (2024-09-16T16:04:16Z)
• Language-universal phonetic encoder for low-resource speech recognition [28.21805271848413]
  We leverage International Phonetic Alphabet (IPA) based language-universal phonetic model to improve low-resource ASR performances. Our approach and adaptation are effective on extremely low-resource languages, even within domain- and language-mismatched scenarios.
  arXiv  Detail & Related papers  (2023-05-19T10:24:30Z)
• Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation [20.45373308116162]
  This study focuses on four typologically diverse minority languages or language variants (West Germanic: Gronings, West-Frisian; Malayo-Polynesian: Besemah, Nasal). For all four languages, we examine the use of self-training, where an ASR system trained with the available human-transcribed data is used to generate transcriptions, which are then combined with the original data to train a new ASR system. We find that using a self-training approach consistently yields improved performance (a relative WER reduction up to 20.5% compared to using an ASR system trained on 24 minutes of
  arXiv  Detail & Related papers  (2023-05-18T13:20:38Z)
• 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)
• Data Augmentation for Low-Resource Quechua ASR Improvement [2.260916274164351]
  Deep learning methods have made it possible to deploy systems with word error rates below 5% for ASR of English. For so-called low-resource languages, methods of creating new resources on the basis of existing ones are being investigated. We describe our data augmentation approach to improve the results of ASR models for low-resource and agglutinative languages.
  arXiv  Detail & Related papers  (2022-07-14T12:49:15Z)
• ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion [49.617722668505834]
  We show that our approach permits the application of speech synthesis and voice conversion to improve ASR systems using only one target-language speaker during model training. It is possible to obtain promising ASR training results with our data augmentation method using only a single real speaker in a target language.
  arXiv  Detail & Related papers  (2022-03-29T11:55:30Z)
• Bootstrap an end-to-end ASR system by multilingual training, transfer learning, text-to-text mapping and synthetic audio [8.510792628268824]
  bootstrapping speech recognition on limited data resources has been an area of active research for long. We investigate here the effectiveness of different strategies to bootstrap an RNN-Transducer based automatic speech recognition (ASR) system in the low resource regime. Our experiments demonstrate that transfer learning from a multilingual model, using a post-ASR text-to-text mapping and synthetic audio deliver additive improvements.
  arXiv  Detail & Related papers  (2020-11-25T13:11:32Z)
• LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition [148.43282526983637]
  We develop LRSpeech, a TTS and ASR system for languages with low data cost. We conduct experiments on an experimental language (English) and a truly low-resource language (Lithuanian) to verify the effectiveness of LRSpeech. We are currently deploying LRSpeech into a commercialized cloud speech service to support TTS on more rare languages.
  arXiv  Detail & Related papers  (2020-08-09T08:16:33Z)
• Streaming End-to-End Bilingual ASR Systems with Joint Language Identification [19.09014345299161]
  We introduce streaming, end-to-end, bilingual systems that perform both ASR and language identification. The proposed method is applied to two language pairs: English-Spanish as spoken in the United States, and English-Hindi as spoken in India.
  arXiv  Detail & Related papers  (2020-07-08T05:00:25Z)
• That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages [72.9927937955371]
  We use the resources existing in other languages to train a multilingual automatic speech recognition model. We observe significant improvements across all languages in the multilingual setting, and stark degradation in the crosslingual setting. Our analysis uncovered that even the phones that are unique to a single language can benefit greatly from adding training data from other languages.
  arXiv  Detail & Related papers  (2020-05-16T22:28:09Z)

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.