Mandarin Speech Data

Data is the “fuel”that drives AI system towards continuous progress, but building high-quality datasets isn’t easy. The part where involve data collecting, cleaning, annotating, and privacy protecting are all challenging. Researchers need to collect targeted data to deal with complex problems faced on different fields to make sure the trained models have robustness and generalization capability. Through using rich datasets, AI system can achieve intelligent decision-making in more complex scenario.

Speech recognition technology has made significant advancements in recent years, revolutionizing the way we interact with computers and devices. One language that has presented both challenges and opportunities in this field is Mandarin Chinese. With its vast vocabulary, tonal nature, and complex phonetics, developing accurate and efficient speech recognition systems for Mandarin Chinese has been a fascinating endeavor.

Mandarin Chinese is the most widely spoken language globally, with over a billion native speakers. As a tonal language, it relies on pitch variations to distinguish between different words, which adds an extra layer of complexity to speech recognition systems. Furthermore, Mandarin Chinese consists of thousands of characters and a vast number of homophones, making it crucial for speech recognition algorithms to accurately decipher the intended words based on context and tonal cues.

Over the years, researchers and engineers have dedicated their efforts to overcoming these challenges. They have developed sophisticated algorithms and deep learning models to train speech recognition systems specifically for Mandarin Chinese. These models utilize vast amounts of data, including recordings of native Mandarin speakers, to learn the intricacies of the language's phonetics and tonal variations.

To enhance the accuracy of speech recognition in Mandarin Chinese, researchers have employed various techniques. One such approach is tone normalization, which involves adjusting the pitch contours of spoken words to minimize tonal variations. This technique helps in reducing errors caused by incorrect tone identification, leading to more precise recognition results.

Additionally, context modeling plays a crucial role in Mandarin Chinese speech recognition. Given the high number of homophones, accurately predicting the intended word based on the surrounding context becomes vital. Researchers have explored language models that incorporate both local and global context to improve accuracy and disambiguation.

Another aspect that researchers have focused on is speaker adaptation. Recognizing speech accurately requires the system to adapt to individual speakers, as everyone has their unique speech patterns and pronunciation. By incorporating speaker adaptation techniques, speech recognition systems can become more personalized and deliver improved results for Mandarin Chinese speakers.

The advancements in speech recognition technology for Mandarin Chinese have opened up a wide range of applications. From voice assistants and dictation software to language learning tools and transcription services, these systems have greatly benefited both individuals and businesses. They have streamlined processes, improved accessibility, and provided new avenues for communication and interaction.

While speech recognition technology for Mandarin Chinese has come a long way, there is still room for improvement. Ongoing research and development continue to refine these systems, aiming for even higher accuracy and usability. As the technology progresses, it will contribute to breaking language barriers and facilitating seamless communication in our increasingly interconnected world.

Nexdata Mandarin Speech Data

1,505 Hours-Mandarin Speech by Mobile Phone

It collects 6,278 speakers' data from 33 provinces of China. 2,980 males and 3,298 females. The recording contents are commonly used colloquial sentences. It is recorded in both quiet and noisy environment. Annotated texts are transcribed and proofread by professional annotators. The accuracy is not less than 98%.

200 People - Chinese Wake-up Words Speech Data by Mobile Phone

Chinese wake-up words audio data captured by mobile phone, collected from 200 people, 180 sentences per person, a total length of 24.5 hours; recording staff come from seven dialect regions with balanced gender distribution; collection environment was diversified; recorded text includes wake-up words and colloquial sentences.

1,420 Hours- Mandarin Spontaneous Speech Data by Mobile Phone

The 1,420 Hours - Mandarin Spontaneous Speech Data is collected by phone and professional audio recorder involved 700 native Chinese speakers in China, 65% of the participants are female, Conversation is conducted through recording phone calls, this data is labeled for the near-end speech audio, and the speech content to close to causal conversation. The accuracy rate of sentences is ≥ 95%.

521 People - Mandarin Voiceprint Recognition Speech Data by Mobile Phone

Each person's time span is very long, which can better cover the sound features of a person in different periods and different states.

2,657 Hours - Mandarin Mobile Telephony Conversational Speech Collection Data

The 2,657 Hours - Mandarin Mobile Telephony Conversational Speech Collection Data involved 4,491 native speakers. 63% of which are female. Speakers conduct conversations without topic limit to ensure the dialogue's 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 and other more attributes are also annotated. The accuracy rate of sentences is ≥ 97%.

In the future data-driven era, the development prospects of artificial intelligence are infinite, and data is still a core factor for AI to unleash its full potential. By building richer datasets and advanced annotation technology, we can certainly promote more breakthroughs in AI in all walks of life. If you have data requirements, please contact Nexdata.ai at [email protected].