Real-Time Speech Accent Correction With Acoustic-Linguistic Synthesis
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Solution Overview
Problem
Existing solutions for correcting accents in audio signals are not effective in real-time communications, leading to difficulties in understanding speakers with strong accents.
Innovation Solution
A computational graph pipeline is used to extract acoustic and linguistic features from input audio signals, synthesizing a spectrum representation lacking the accent, and generating an output audio signal using a vocoder, with parallel processing on GPUs, CPUs, and FPGAs to reduce delay to 40-300 milliseconds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing accent correction solutions are used, then accent correction capability is provided, but real-time processing effectiveness deteriorates
Solution Approach 1:
The audio signal is divided into overlapping frames of 20-30 milliseconds each, allowing parallel processing of multiple frames simultaneously. This segmentation enables the system to process audio in manageable chunks while maintaining real-time performance through parallel computation across multiple frames.
Solution Approach 2:
The system transforms the audio signal from time-domain representation to frequency-domain representation using Fourier transform. This dimensional transformation enables accent correction in the frequency domain while maintaining real-time processing capability, resolving the contradiction between correction effectiveness and processing speed.
2Manufacturing precision
If complex computational processing is applied, then accent correction quality is improved, but processing delay increases
Solution Approach 1:
Acoustic features such as pitch, energy, and spectral characteristics are extracted in advance from each audio frame before accent correction is applied. This preliminary feature extraction enables the correction algorithm to work with pre-processed data, reducing overall processing delay while maintaining correction quality.
Solution Approach 2:
Traditional time-consuming accent correction methods are replaced with neural network-based models that can perform complex transformations more efficiently. The neural networks are trained offline to learn accent patterns, enabling fast real-time inference that maintains high correction quality without excessive processing delay.
Data Source
AI summary
Systems and methods for real-time correction of an accent in an input audio signal are provided. A method includes extracting acoustic features from a chunk of a stream of chunks of the input audio signal by an acoustic features extraction module of a computational graph; extracting, by a linguistic features extraction module of the computational graph, linguistic features with a reduced accent from the chunk; synthesizing, by a synthesis module of the computational graph, a spectrum representation based on the acoustic features, the linguistic features, and a speaker embedding for a human speaker; and generating, by a vocoder of the computational graph and based on the spectrum representation, an output chunk of an output audio signal. The input audio signal is digitized with a first sample rate and the output audio signal is digitized with a second sample rate.