OpenAI's next-generation audio model: redefining the AI ​​voice interaction experience

OpenAI recently released new next-generation audio models, including gpt-4o-transcribe, gpt-4o-mini-transcribe, and gpt-4o-mini-tts, which have achieved significant breakthroughs in speech recognition and synthesis technology. These models not only provide more accurate transcription and more natural speech synthesis, but also completely change the way people interact with computers. These new audio technologies are open to developers around the world through APIs, opening up new possibilities for building smarter and more natural voice applications.

Overview of the New Audio Model

OpenAI's next-generation audio models include two speech-to-text models (gpt-4o-transcribe and gpt-4o-mini-transcribe) and a text-to-speech model (gpt-4o-mini-tts). These models have achieved significant improvements in many aspects, marking a major leap forward in the field of speech technology.

gpt-4o-transcribe and gpt-4o-mini-transcribe show significant improvements in transcription accuracy, especially under difficult conditions such as noisy environments, diverse accents, and varying speech rates. This makes them more suitable for real-world applications such as transcribing customer service calls or meetings. Benchmark tests using the multilingual FLEURS dataset show that these models consistently outperform Whisper v2 and v3, as well as competing systems such as Gemini and Nova.

On the other hand, the GPT-4O-Mini-TTS model enables precise control over speech generation, allowing developers to control not only “what to say” but also “how to say it.” This provides developers with unprecedented possibilities, allowing for more personalized and expressive AI-generated speech, from empathetic customer service voices to creative storytelling experiences.

Technological innovation and working principle

Unified architecture design

Unlike traditional multimodal models, GPT-4o is designed with a single Transformer architecture. Traditional models usually design encoders and decoders for different modalities, while GPT-4o unifies data from all modalities into one neural network for processing. The core of this architecture is the Transformer, which processes input sequence data, whether it is text, image or audio, through the self-attention mechanism.

This unified processing method avoids the problem of inefficient information fusion caused by separate processing of different modal information in traditional methods. The innovation of GPT-4o lies in its early fusion strategy, which maps all modal data into a common representation space from the beginning of training, allowing the model to naturally process and understand cross-modal information.

Three major technological breakthroughs

According to OpenAI, the technical innovations of these new audio models include three key aspects:

1. Pre-trained on professional audio datasets : These models are built on the GPT-4o and GPT-4o-mini architectures and are extensively pre-trained on specialized audio datasets. This targeted approach provides a deep understanding of the nuances of speech, enabling the models to excel in audio-related tasks.
2. Advanced distillation methods : OpenAI has improved knowledge distillation techniques, enabling large audio models to effectively transfer knowledge to smaller, more efficient models. By adopting advanced self-play methods, the distillation dataset successfully captures the real conversation dynamics and simulates real user interactions with the assistant. This helps smaller models excel in conversation quality and responsiveness.
3. Reinforcement Learning Paradigm : For speech-to-text models, OpenAI introduced a paradigm based on reinforcement learning (RL) to push transcription accuracy to the state-of-the-art. This approach significantly improves accuracy and reduces hallucinations, making speech-to-text solutions highly competitive in complex speech recognition scenarios.

Performance and Advantages Comparison

Significant latency reduction

These new models achieve significant latency reductions compared to previous techniques. Before GPT-4o, ChatGPT’s speech mode had an average latency of 2.8 seconds (GPT-3.5) and 5.4 seconds (GPT-4). Speech mode consists of a pipeline of three separate models: a simple model that transcribes audio to text, GPT-3.5 or GPT-4 that takes in text and outputs text, and a third simple model that converts that text back into audio.

GPT-4o can respond to audio input in as little as 232 milliseconds and an average of 320 milliseconds, which is comparable to the response time of humans in a conversation. This low latency makes interaction with AI more natural and smooth, creating conditions for real-time interactive applications.

Multi-language performance improvements

These models have also made significant breakthroughs in multilingual processing. GPT-4o's language segmentation performance shows that compared with previous models, the new model has achieved amazing efficiency improvements on non-English languages: Gujarati reduces the number of tokens by 4.4 times, Telugu by 3.5 times, Tamil by 3.3 times, Marathi and Hindi by 2.9 times respectively.

This efficiency improvement not only reduces processing costs, but also improves the processing quality and accuracy of non-English languages, providing a better voice interaction experience for global users.

State-of-the-art speech understanding

The new generation of audio models has made significant progress in speech understanding. In multiple languages, they achieve lower word error rates (WER), are more accurate than OpenAI's original Whisper model, and can better understand human speech. This improvement enables the model to work in more complex environments, such as environments with background noise, speech with various accents, and scenes with technical language.

Wide range of application scenarios

Customer service and interactive experience

These audio models are revolutionizing the customer service space. They enable developers to create voice agents capable of real-time voice interactions, or AI-driven systems that can operate independently to assist users through spoken interactions, with applications ranging from customer care to language learning.

For example, in call center scenarios, these models can provide more accurate real-time transcription, help customer service staff better understand customer needs, and provide more accurate answers with AI assistance. In automatic voice response systems, they can provide a more natural and humanized interactive experience, greatly improving user satisfaction.

Content Creation and Media Production

The new text-to-speech model (gpt-4o-mini-tts) provides a powerful tool for content creators. With its controllability, creators can generate speech content with specific styles and emotional colors, which is suitable for scenarios such as audiobook production, podcast creation, and advertising dubbing.

Developers can instruct the model not only "what to say" but also "how to say it", for example, they can instruct the model to "speak like a compassionate customer service representative", enabling unprecedented customized experiences. This opens up new possibilities for storytelling, educational content, and entertainment media creation.

Accessibility and translation services

In the field of accessibility, these models provide more accurate real-time transcription capabilities, helping the hearing-impaired to better participate in social activities and work discussions. At the same time, their multilingual support and accurate transcription capabilities make cross-language communication smoother.

OpenAI's demonstration video shows how the model can achieve real-time translation, allowing people who speak different languages ​​to communicate without barriers. This capability not only helps personal communication, but also facilitates international conferences, multilingual teaching, and global business cooperation.

Meeting Minutes and Business Collaboration

In business settings, these models can significantly improve meeting efficiency by being able to transcribe meeting content in real time, generate high-quality meeting records, identify different speakers, and extract key information points.

This not only saves time on manually recording meetings, but also ensures that information is accurately captured, allowing meeting participants to focus more on the discussion itself. This feature is especially important for remote and hybrid work environments, helping all team members stay in sync, no matter where they are.

The development of OpenAI audio technology

OpenAI's development in the audio field has gone through several important stages, each of which laid the foundation for the current breakthrough:

• 2019: The MuseNet music generation tool based on GPT-2 was released, which can create music works of different styles and genres
• 2022: Release of the first audio model, Whisper, an automatic speech recognition system trained on 680,000 hours of multilingual data
• Around 2023: Jukebox music generation model and TTS-1 text-to-speech model released, TTS-1 provides six voices and multi-language support
• May 13, 2024: GPT-4o is released as the first multimodal model capable of processing audio, vision, and text in real time
• March 20, 2025: Next-generation audio models (gpt-4o-transcribe, gpt-4o-mini-transcribe, and gpt-4o-mini-tts) released in the API

From OpenAI's development path, we can see that the company has adopted a progressive innovation strategy in the field of audio technology, constantly improving and optimizing its models to make them more accurate, natural and practical. Alexis Conneau, OpenAI's head of audio research, vividly described his work: "Giving the GPT model a talking mouth."

Future Outlook and Development Trends

Deep integration of multimodal interaction

In the future, AI audio technology will be further integrated with other modalities to achieve more natural and intuitive human-computer interaction. This multimodal interaction will not only be limited to voice, but will also integrate text, images, videos and other information to create a richer and more immersive user experience.

With the continuous innovation of algorithms and continuous optimization of models, AI will be able to understand human language more accurately and generate more natural and fluent responses. At the same time, miniaturization and lightweighting of models will also become a development trend to reduce deployment costs and improve operational efficiency.

Wide expansion of application areas

AI audio technology will play a key role in more areas:

1. Smart home and Internet of Things: Through voice interaction, smart home equipment control, information query and entertainment services can be realized, improving the convenience and intelligence of family life.
2. Healthcare: As an important tool for auxiliary diagnosis, health management and personalized treatment, through voice interaction, patients can easily obtain health consultation, appointment registration, medicine purchase and other services.
3. Education and training: AI has become an important assistant for personalized learning and intelligent tutoring. Students can access learning resources and answer questions anytime and anywhere, and teachers can also use AI to grade homework and evaluate teaching effectiveness.
4. Financial services: used for risk assessment, investment decision-making and fraud detection. Customers can easily access functions such as financial consulting, account management and transaction services.

Emotional understanding and autonomous decision making

Future audio AI will achieve breakthroughs in emotional understanding. With the development of affective computing technology, AI will be able to better understand and respond to human emotional needs, and identify and respond to users' emotional states through subtle clues such as intonation, rhythm, and pauses in speech.

At the same time, AI will have higher autonomous decision-making and execution capabilities, and will be able to autonomously adjust strategies and behaviors according to changes in the environment and tasks, to achieve more intelligent and autonomous services, and provide users with a more personalized and intelligent experience.

Ethics and Privacy Protection

With the widespread application of AI audio technology, data security and privacy protection will become important issues. It is necessary to establish a sound data management and protection mechanism to ensure the security and privacy of user voice data.

At the same time, the application of AI involves issues of ethics and social fairness, and relevant ethical norms and regulatory policies need to be formulated to ensure that the application of AI complies with social values ​​and laws and regulations, and to prevent abuse of technology and discriminatory applications.

Last words

OpenAI's next-generation audio models represent a major leap forward in AI voice technology, achieving significant breakthroughs in speech recognition and synthesis through a unified architecture design, end-to-end training methods, and innovative technical means. These models not only improve transcription accuracy and the naturalness of speech synthesis, but also significantly reduce response latency, making AI's voice interaction experience closer to natural human communication.

As these technologies continue to develop and their application scenarios expand, we can foresee that AI will play an increasingly important role in many areas, including customer service, content creation, accessibility, and business collaboration. At the same time, we need to pay attention to issues such as data security, privacy protection, and ethical norms to ensure that the development direction of AI technology is in line with the fundamental goal of human well-being.

OpenAI's next-generation audio model not only demonstrates the highest level of current AI voice technology, but also provides an important reference for the future development direction of human-computer interaction. With the further development and improvement of technology, we have reason to believe that a more natural, intelligent and humanized AI voice interaction experience will become a reality in the near future.