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

Brazilian Portuguese Speech Data

From:Nexdata Date:2023-11-19

Brazilian Portuguese, a variant of the Portuguese language spoken in Brazil, is known for its unique phonetic characteristics and regional dialects. As technology continues to evolve, there has been a growing interest in developing advanced speech recognition systems tailored specifically for Brazilian Portuguese. This article explores the recent advancements in Brazilian Portuguese speech recognition technology, highlighting the challenges faced and the potential impact on various industries.

 

Challenges in Brazilian Portuguese Speech Recognition

 

Developing robust speech recognition systems for Brazilian Portuguese comes with its own set of challenges. The language's diverse accents, regional variations, and informal speech patterns make it more complex compared to European Portuguese or other languages. Additionally, the lack of a standardized pronunciation and the influence of indigenous languages contribute to the intricacies of accurate speech recognition.

 

Applications in Various Industries

The impact of improved Brazilian Portuguese speech recognition technology extends to various industries. In the healthcare sector, for example, accurate speech recognition can enhance medical transcription services, streamline documentation processes, and improve communication between healthcare professionals.

 

In education, speech recognition can facilitate language learning by providing interactive and personalized language practice for learners. Additionally, in customer service and business communication, advanced speech recognition can enhance voice-controlled systems, virtual assistants, and call center operations, leading to improved customer experiences.

 

Nexdata Brazilian Portuguese Speech Data

 

1,044 Hours - Brazilian Portuguese Speech Data by Mobile Phone

 

The 1,044 Hours - Brazilian Portuguese Speech Data of natural conversations collected by phone involved more than 2,038 native speakers, developed with proper balance of gender ratio and geographical distribution. Speakers would choose linguistic experts designed topics conduct conversations. 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 transcript with text content, the start and end time of each effective sentence, and speaker identification. The accuracy rate of sentences is ≥ 95%.

 

127 Hours - Brazilian Portuguese Conversational Speech Data by Mobile Phone

 

The 127 Hours - Brazilian Portuguese Conversational Speech Data involved 142 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.

 

104 Hours - Brazilian Portuguese Conversational Speech Data by Telephone

 

104 Hours - Brazilian Portuguese Conversational Speech Data by Telephone involved 118 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, u-law pcm, 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.

befcb97f-b5f4-4b12-a79c-7a75256267b8