en

Please fill in your name

Mobile phone format error

Please enter the telephone

Please enter your company name

Please enter your company email

Please enter the data requirement

Successful submission! Thank you for your support.

Format error, Please fill in again

Confirm

The data requirement cannot be less than 5 words and cannot be pure numbers

Harnessing Off-the-Shelf Datasets for Powerful AI Solutions

From:Nexdata Date: 2024-08-14

Speech recognition technology has made significant advancements in recent years, revolutionizing various aspects of our lives. One particular area where this technology has had a profound impact is in the Filipino community.

Filipino, as the national language of the Philippines, is spoken by millions of people both in the country and across the globe. However, the complexity of the Filipino language, with its rich vocabulary and diverse accents, has posed challenges for speech recognition systems in accurately transcribing spoken words.

Fortunately, researchers and developers have recognized the importance of addressing this issue and have been working tirelessly to improve Filipino speech recognition technology. Through the use of advanced machine learning algorithms and extensive data sets, these efforts have resulted in remarkable progress.

Nexdata Filipino Speech Data

522 Hours - Filipino Speech Data by Mobile Phone

522 Hours - Filipino Speech Data by Mobile Phone,the data were recorded by Filipino speakers with authentic Filipino accents.The text is manually proofread with high accuracy. Match mainstream Android, Apple system phones.

104 Hours - Filipino Conversational Speech Data by Mobile Phone

The 104 Hours - Filipino Conversational Speech Data by Mobile Phone collected by phone involved 140 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.

350cbb63-9f9e-4bd5-b078-f2b666a78250