Server-Side Speech Signal Processing for Packet Loss Recovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication systems experience signal loss due to packet loss during data transmission, leading to degraded sound quality and incomplete audio reception, with current error correction methods on recipient devices being insufficient in accuracy and requiring installation on each device for all participants in a session.
Innovation Solution
Implementing speech signal processing on servers managing communications sessions using a hidden Markov model to detect missing data, calculate and select possible content to fill gaps, and update the signal, achieving high accuracy in data recovery and universal error correction across all participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error correction is implemented on recipient devices, then each device can independently correct missing packets, but the accuracy is insufficient and requires installation on each device for all participants
Solution Approach 1:
Instead of implementing error correction on recipient devices (traditional approach), the patent inverts the approach by implementing speech-to-text transcription and error correction on the server side. The server processes the audio signal, generates text, corrects errors in the text domain, and synthesizes corrected audio, thereby achieving higher accuracy without requiring client-side installations.
Solution Approach 2:
The patent introduces text as an intermediary medium between audio transmission and reception. The audio signal is transcribed to text on the server, corrected in the text domain using language models and contextual analysis, then synthesized back to audio. This intermediary step enables more accurate error correction compared to direct audio processing on client devices.
2Measurement precision
If speech transcription is used to recover lost data, then high accuracy can be achieved, but the processing complexity increases
Solution Approach 1:
The patent uses text as an intermediary representation that simplifies error correction. Speech transcription converts complex audio signals into discrete text tokens, making it easier to apply language models, contextual analysis, and probabilistic methods for error detection and correction. The text domain is more amenable to algorithmic processing than the continuous audio domain.
Solution Approach 2:
The patent replaces traditional audio signal processing methods with speech-to-text transcription and text-to-speech synthesis. This substitution leverages advanced natural language processing and machine learning models that operate in the discrete text domain, achieving higher accuracy than conventional audio-based error correction methods.
3Ease of manufacture
If conventional error correction methods are used, then implementation is simpler, but the correction rate does not exceed conventional rates and requires individual device installation
Solution Approach 1:
The patent implements a universal error correction system on the server that serves all participants in a communication session simultaneously. The server-side transcription and correction infrastructure benefits all connected devices without requiring individual installations or configurations on each client device, achieving both universality and high effectiveness.
Solution Approach 2:
The server automatically performs speech transcription, error detection, and correction without requiring user intervention or client-side software. The system self-manages the entire error correction process, from receiving the audio signal to generating corrected output, eliminating the need for users to install or configure error correction tools on their devices.
Data Source
AI summary
The present disclosure relates to systems and methods for speech signal processing on a signal to transcribe speech. In one implementation, the system may include a memory storing instructions and a processor configured to execute the instructions. The instructions may include instructions to receive the signal, determine if at least a portion of data in the signal is missing, and when at least a portion of data is missing: process the signal using a hidden Markov model to generate an output; using the output, calculate a set of possible contents to fill a gap due to the missing data portion, with each possible content having an associated probability; based on the associated probabilities, select one of the set of possible contents; and using the selected possible content, update the signal.