Empowering Minority Language Speech Recognition through Datasets

With the rapid development of artificial intelligence technology, data has become the main factor in various artificial intelligence applications. From behavior monitoring to image recognition, the performance of artificial intelligence systems is highly dependent on the quality and diversity of data sets. However, in the face of massive data demands, how to collect and manage this data remains a huge challenge.

Minority languages often face challenges stemming from limited resources, diminished intergenerational transmission, and lack of recognition. This threatens their survival and the cultural diversity they represent. However, modern advancements in technology, particularly in the realm of data resources and speech recognition, are proving to be pivotal tools in safeguarding these languages.

Data resources play a vital role in documenting and studying minority languages. By amassing written texts, audio recordings, and multimedia content, linguists and researchers can build comprehensive linguistic databases. These databases capture the nuances of phonetics, grammar, vocabulary, and cultural context. This wealth of information not only ensures the preservation of these languages but also facilitates their study and analysis.

Speech recognition technology, fueled by machine learning and artificial intelligence, has the potential to bridge language barriers and give a voice to minority languages. Through speech recognition applications, these languages can be transcribed, translated, and shared more widely. This technology not only aids linguists in their research but also enables fluent speakers to engage with and contribute to the preservation process.

Collaboration among various stakeholders is crucial. Governments and organizations should allocate resources for language documentation projects, encouraging the collection and digitization of data resources. Native speakers and local communities are essential in providing linguistic expertise and cultural insights. Linguists and technology experts work hand in hand to develop accurate speech recognition models that can understand and transcribe minority languages effectively.

Moreover, the intersection of data resources and speech recognition goes beyond preservation. It enables the creation of interactive language learning tools and digital platforms. These platforms can offer immersive experiences for learners, helping to bridge the gap between generations and rekindle interest in the language. Speech recognition-powered language apps can facilitate real-time conversations, aiding learners in pronunciation and communication.

Nexdata Minority Language Speech Datasets

120 Hours - Burmese Conversational Speech Data by Mobile Phone

The 120 Hours - Burmese Conversational Speech Data involved more than 130 native speakers, developed with proper balance of gender ratio, Speakers would choose a few familiar topics out of the given list and start conversations to ensure dialogues' fluency and naturalness. The recording devices are various mobile phones. The audio format is 16kHz, 16bit, uncompressed WAV, and all the speech data was recorded in quiet indoor environments. All the speech audio was manually transcribed with text content, the start and end time of each effective sentence, and speaker identification.

320 Hours - Dari Conversational Speech Data by Telephone

The 320 Hours - Dari Conversational Speech Data collected by telephone involved more than 330 native speakers, developed with proper balance of gender ratio, Speakers would choose a few familiar topics out of the given list and start conversations to ensure dialogues' fluency and naturalness. The recording devices are various mobile phones. The audio format is 8kHz, 8bit, WAV, and all the speech data was recorded in quiet indoor environments. All the speech audio was manually transcribed with text content, the start and end time of each effective sentence, and speaker identification.

200 Hours - Urdu Conversational Speech Data by Telephone

The 200 Hours - Urdu Conversational Speech Data collected by telephone involved more than 230 native speakers, developed with proper balance of gender ratio, Speakers would choose a few familiar topics out of the given list and start conversations to ensure dialogues' fluency and naturalness. The recording devices are various mobile phones. The audio format is 8kHz, 8bit, WAV, and all the speech data was recorded in quiet indoor environments. All the speech audio was manually transcribed with text content, the start and end time of each effective sentence, and speaker identification.

200 Hours - Pushtu Conversational Speech Data by Telephone

The 200 Hours - Pushtu Conversational Speech Data collected by telephone involved more than 230 native speakers, developed with proper balance of gender ratio, Speakers would choose a few familiar topics out of the given list and start conversations to ensure dialogues' fluency and naturalness. The recording devices are various mobile phones. The audio format is 8kHz, 8bit, WAV, and all the speech data was recorded in quiet indoor environments. All the speech audio was manually transcribed with text content, the start and end time of each effective sentence, and speaker identification.

Data-driven AI transformation is deeply affecting our ways of life and working methods. The dynamic nature of data is the key for artificial intelligent models to maintain high performance. Through constantly collecting new data and expanding the existing ones, we can help models better cope with new problems. If you have data requirements, please contact Nexdata.ai at [email protected].