Voice Packet Reconstruction via Natural Language Generation
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Solution Overview
Problem
Current audio and video conferencing systems face challenges in maintaining quality audio during network fluctuations, leading to lost or misunderstood context due to dropped voice packets, which results in a poor user experience.
Innovation Solution
The conferencing system employs natural language generation to reconstruct missing voice packets by converting audio data to text in real-time, using a language model to generate new phrases based on partial text and context, and then synthesizing these phrases back into voice packets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voice packets are transmitted during network fluctuations, then audio data may be lost due to dropped packets, but retransmitting or reconstructing packets consumes additional computing and networking resources
Solution Approach 1:
The system performs preliminary actions by converting audio data to text in real-time before packet loss occurs, and maintains a context buffer of previous text data. When packet loss is detected, the pre-prepared text and context are immediately used to generate replacement packets, eliminating the need for resource-intensive post-loss recovery operations.
Solution Approach 2:
The patent introduces text data as an intermediary between audio input and audio output. Audio is converted to text, processed through language models to generate coherent phrases even during packet loss, then converted back to audio. This intermediary representation enables intelligent reconstruction without directly manipulating audio packets.
2Loss of information
If voice packets are dropped during network signal loss, then context is lost or misunderstood, but reconstructing packets in real-time increases system complexity
Solution Approach 1:
The system creates a copy of the audio information in text form, which can be manipulated independently of the original audio packets. This text copy serves as a resilient representation that can be regenerated from context even when audio packets are lost, simplifying the reconstruction process compared to direct audio manipulation.
Solution Approach 2:
The system implements feedback by continuously monitoring network conditions and packet reception status. When packet loss is detected, the feedback loop triggers the language model to generate replacement text based on context, which is then converted to audio and inserted into the stream, creating a closed-loop error correction system.
3Ease of operation
If natural language generation is used to reconstruct missing packets, then user experience is improved, but computing resources are consumed for real-time text processing and synthesis
Solution Approach 1:
The system applies partial action by processing only the portions of audio that are affected by packet loss, rather than continuously processing all audio through the full text conversion and language model pipeline. The language model generates only the missing phrases needed to complete the context, reducing overall computational load while maintaining user experience quality.
Data Source
AI summary
A device may receive and convert audio data to text data in real-time, and may detect a network fluctuation that causes missing voice packets. The device may process partial text and context of the text data, with a model, to generate a new phrase, and may generate a response phoneme for the new phrase. The device may utilize a text embedding model to generate a text embedding for the response phoneme, and may process the audio data, with the model, to generate a target voice sequence. The device may utilize an audio embedding model to generate an audio embedding for the target voice sequence, and may combine the text embedding and the audio embedding to generate an embedding input vector. The device may process the embedding input vector, with an audio synthesis model, to generate a final voice response, and may provide the audio data and the final voice response.