Solving the Complexities of Korean Speech Recognition With Training Data

With the widespread machine learning technology, data’s importance shown. Datasets isn’t just provide the foundation for the architecture of AI system, but also determine the breadth and depth of applications. From anti-spoofing to facial recognition, to autonomous driving, perceived data collection and processing have become a prerequisites for achieving technological breakthroughs. Hence, high-quality data sources are becoming an important asset for market competitiveness.

In the realm of Korean speech recognition, a myriad of challenges has surfaced, making the creation and utilization of Korean speech datasets a crucial but complex endeavor.

Challenges in Pronunciation:

One of the primary hurdles in Korean speech recognition lies in the nuances of pronunciation. The Korean language comprises various vowel and consonant combinations, intricate intonations, and subtle phonetic differences that significantly impact the accuracy of automatic speech recognition (ASR) systems. Building a comprehensive dataset that captures this phonetic diversity is essential for training models capable of accurately transcribing spoken Korean.

Dialectical Variations:

Korean is spoken across different regions, each with its own distinct dialects and regionalisms. Integrating these variations into a cohesive speech dataset poses a considerable challenge. Recognition models need to be exposed to a diverse range of accents and linguistic peculiarities to ensure robust performance across all Korean-speaking communities.

Cultural Context and Informality:

Korean, like many languages, often includes informal and context-dependent expressions. Capturing the cultural context embedded in informal speech adds another layer of complexity to Korean speech datasets. Striking a balance between formal and informal language use is crucial to the effectiveness of speech recognition systems, particularly in real-world, conversational scenarios.

Continuous Evolution of Language:

Languages are dynamic entities that evolve over time, incorporating new vocabulary, expressions, and linguistic trends. Korean is no exception, with language evolution driven by cultural shifts, technological advancements, and global influences. Maintaining the relevance of Korean speech datasets necessitates regular updates and adaptations to reflect contemporary language usage accurately.

Nexdata Korean Speech Recognition Dataset

136 Hours - Korean Conversational Speech Data by Telephone

The 136 Hours - Korean Conversational Speech Data by Telephone involved 216 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 audio format is 8kHz, 8bit, 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.

290 Hours - Korean Conversational Speech Data by Mobile Phone

The 290 Hours - Korean Conversational Speech Data by Mobile Phone collected by phone involved 442 native speakers, developed with a proper balance of gender ratio. Speakers would choose a few familiar topics out of the given list and start conversations 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. The accuracy rate of sentences is ≥ 95%.

516 Hours - Korean Speech Data by Mobile Phone

The 516 Hours - Korean Speech Data of natural conversations collected by phone involved more than 1,077 native speakers, ehe duration of each speaker is around half an hour. developed with proper balance of gender ratio and geographical distribution. 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%.

93 Hours Korean(Korea) Children Real-world Casual Conversation and Monologue speech dataset

93 Hours Korean(Korea) Children Real-world Casual Conversation and Monologue speech dataset, covers self-media, conversation, live, lecture, variety show and other generic domains, mirrors real-world interactions. Transcribed with text content, speaker's ID, gender, age, accent and other attributes. Our dataset was collected from extensive and diversify speakers(12 years old and younger children), geographicly speaking, enhancing model performance in real and complex tasks.rnQuality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

393 Hours - Korean Children Speech Data by Mobile Phone

Mobile phone captured audio data of Korean children, with total duration of 393 hours. 1085 speakers are children aged 6 to 15; the recorded text contains common children's languages such as essay stories, and numbers. All sentences are manually transferred with high accuracy.

With the continuous advance of data technology, we can look expect more innovative AI applications emerge in all walks of life. As we mentioned at the beginning, the importance of data in AI cannot be ignored, and high-quality data will continuously drive technological breakthroughs.