Portuguese Speech Data

In the progress of constructing an intelligent future, datasets play a vital role. From autonomous driving cars to smart security systems, high-quality datasets provide AI models with massive amount of learning materiel, empowering AI model more adaptable in various real-world scenarios. Companies and researchers through continuously improving the efficiency of data collection and annotation can accelerate the implementation of AI technology, help all industries achieve their digital transformation.

Portuguese is one of the most widely spoken languages in the world, with a significant presence in countries such as Portugal, Brazil, Angola, Mozambique, and others. However, due to its unique phonetic characteristics and language variations, Portuguese speech recognition becomes complex and challenging.

Firstly, Portuguese has a rich inventory of vowels and consonant phonemes, making its phonetic system relatively complex. This poses difficulties for speech recognition systems to accurately distinguish between these phonemes. Acoustic modeling needs to account for subtle differences between different phonemes to avoid incorrect recognition results.

Secondly, Portuguese exhibits regional and dialectal variations across different countries and regions where it is spoken. These variations manifest in terms of pronunciation, intonation, and vocabulary choices. This diversity poses a challenge for speech recognition systems to adapt to and accommodate the different dialects and accents of Portuguese speakers. The system needs to be trained with a wide range of dialectal data to ensure accurate recognition across different linguistic contexts.

Furthermore, Portuguese is characterized by a high degree of coarticulation and assimilation, where sounds can change and be influenced by adjacent sounds. This phenomenon, known as phonetic coarticulation, adds another layer of complexity to Portuguese speech recognition. The system must be designed to capture these coarticulation patterns accurately to improve recognition performance.

Moreover, Portuguese is a morphologically rich language, with various inflections, derivations, and compounds. This complexity affects the language modeling aspect of speech recognition. The system needs to be trained on a diverse and extensive corpus of Portuguese text to capture the language's morphological intricacies accurately.

Nexdata Portuguese Speech Data

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.

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%.

986 Hours - European Portuguese Speech Data by Mobile Phone

It is speech data of 2,109 Portuguese natives with authentic accents. The recorded text is designed by professional language experts and is rich in content, covering multiple categories such as general purpose, interactive, vehicle-mounted and household commands. The recording environment is quiet and without echo. The texts are manually transcribed with a high accuracy rate. Recording devices are mainstream Android phones and iPhones.

All in all, datasets aren’t only the foundation of AI model training, but also the driving force for innovative intelligence solution. With the steady development of data collection technology, we have reason to believe that in the future there will be much more high-quality datasets, to provide a broader space for the application prospects of AI technology. Let’s behold and witness the intersection of data and intelligence.