Nexdata Conversational Speech Data

In the realm of human interaction, conversational speech serves as the glue that binds individuals, forging connections and facilitating understanding. The nuances and intricacies of conversational speech are pivotal in our daily lives, enriching relationships, driving business transactions, and transcending cultural boundaries.

Conversational speech, at its core, is a dynamic and multifaceted form of communication that encompasses spoken words, tone, gestures, and body language. It is a symphony of expression that reflects our emotions, thoughts, and intentions. The power of conversational speech lies in its ability to convey not only information but also empathy, humor, sympathy, and a myriad of human sentiments.

In recent years, technology has brought about a transformative shift in the landscape of conversational speech. Conversational AI, a burgeoning field, is designed to emulate human-like speech patterns and understand the subtleties of language. This technology leverages natural language processing (NLP) algorithms to enable machines to engage in meaningful and context-aware conversations with humans.

The impact of conversational speech technology is profound. It has found applications in various domains, from virtual assistants like Siri and Google Assistant, which facilitate effortless interactions with devices, to customer service chatbots that offer round-the-clock support, and even in language translation services that bridge linguistic gaps. Conversational AI is enhancing accessibility and convenience in ways previously unimagined.

Moreover, conversational speech is breaking down cultural and geographical barriers. Language translation applications, powered by advanced NLP and speech recognition, enable individuals from diverse linguistic backgrounds to engage in meaningful dialogues. This promotes cultural exchange and fosters a global sense of community.

However, the journey to perfecting conversational speech technology is not without its challenges. Ensuring that machines can comprehend context, emotions, and the nuances of human expression remains a complex task. Striking the right balance between automation and genuine human connection is also an ongoing concern.

Nexdata Conversational Speech Data

127 Hours - Malay Conversational Speech Data by Mobile Phone

The 127 Hours - Malay Conversational Speech Data by Mobile Phone collected by phone 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.

100 Hours - Canadian French Conversational Speech Data by Mobile Phone

100 Hours - Canadian French Conversational Speech Data by Mobile Phone involved about 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.

107 Hours - Mexican Spanish Conversational Speech Data by Mobile Phone

107 Hours - Mexican Spanish Conversational Speech Data by Mobile Phone involved 126 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.

93 Hours - Russian Conversational Speech Data by Telephone

93 Hours - Russian Conversational Speech Data collected by telephone involved 126 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.

89 Hours - Indonesian Conversational Speech Data by Telephone

The 89 Hours - Indonesian conversational speech data collected by Telephone involved 124 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.