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Text-to-Speech (TTS) technology has witnessed unprecedented advancements, enabling seamless voice communication between machines and humans. Its transformative impact is evident in various applications, ranging from voice assistants to intelligent customer service and smart homes, seamlessly integrating into our daily lives. The recent ChatGPT update introduces a groundbreaking feature – voice conversation functionality, allowing users to engage in real-time conversations with ChatGPT using synthesized voices, simulating natural phone conversations with instantaneous responses.
As TTS technology becomes an integral part of our lives, there is a growing demand for emotional expressiveness and personalization in machine interactions. Nexdata has responded to this demand by enhancing its personalized voice synthesis capabilities, catering to applications such as virtual assistants, voice readings, videos, and customer service.
I. Pioneering Multimodal AI Data Collection
Nexdata's latest breakthrough is in multimodal voice synthesis, seamlessly combining audio and video perception through facial capture. Leveraging their extensive experience in audio-visual data annotation and collection, coupled with a high-quality synthesis system, they have created a dataset that integrates voice and visual cues. This synchronized AI data service involving multiple participants ensures precise alignment, enhancing emotional expressiveness through facial expressions. The resulting synthesized voices authentically mirror natural dialogues.
II. Abundant Resources
Nexdata boasts a rich pool of professional actors and models cultivated over years of TTS annotation services. These individuals excel in script delivery, possessing exceptional vocal and facial expression skills, ensuring the generation of high-quality data.
Additionally, Nexdata employs professional condenser microphones supporting multi-channel synchronous multimodal data annotation services, ensuring diversity in collection across scenarios, ages, and shooting angles.
III. Expanding Voice Library
In addition to single-person voice libraries, Nexdata introduces a multi-person average model library, broadening voice coverage for enhanced personalization during voice synthesis training.
IV. Innovations in Music Data Collection
Nexdata's TTS processing capabilities now seamlessly integrate musical and language-related information into unified formats, streamlining annotation by extracting key musical elements like pitch and style. Annotation capabilities have expanded to encompass singing styles, refining vocal data processing.
V. Personalized Collection Capabilities
Equipped with a professional TTS recording studio and an extensive library of finished data resources, Nexdata offers personalized voice libraries catering to various tones, roles, and languages, meeting diverse needs like authoritative, friendly, or casual tones.
VI. Scene Restoration Collection Capabilities
Nexdata's dialogue-based TTS AI data annotation services include real-life imitations of interview and customer service scenarios conducted in a professional studio, achieving natural dialogue collection methods for authentic voice reproduction.
VII. Professional Oversight
Each TTS project at Nexdata undergoes scrutiny by professional listening personnel, ensuring recording quality and maintaining high data control standards.
In the era of rapid model development, TTS technology remains at the forefront of refining the user experience. Nexdata's comprehensive system manages the quality and security of TTS datasets, addressing diverse demands for vocal image creation through professional equipment, abundant voice samples, and extensive project experience.
Effective AI training datasets play a pivotal role in advancing autonomous driving technologies within the automotive industry. Overcoming the challenges associated with data collection, labeling, augmentation, and cleaning is crucial for creating high-quality datasets that contribute to the development of safe and reliable self-driving cars. Here's an overview of the key steps involved in tackling these challenges:
Introduction: In the dynamic landscape of the automotive industry, driven by evolving customer demands, car manufacturers are confronted with the imperative of developing advanced multilingual speech recognition systems to elevate the in-cabin voice experience. However, the localization of in-vehicle systems and the effective management of linguistic data across diverse languages present formidable challenges. To overcome these complexities and ensure the availability of high-quality training data, strategic partnerships with technology companies possessing deep language expertise have become indispensable.