End-to-End Image-to-Audio Neural Network for Phoneme Duration Synthesis
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Solution Overview
Problem
Conventional systems for converting images to speech are complex, computationally expensive, and require manual programming, with issues arising at the interface between image-to-text and text-to-speech models due to differences in output sequence lengths.
Innovation Solution
A single end-to-end image-to-audio neural network model is proposed, which includes padding intermediate outputs for consistent length and duration prediction, simplifying hardware design and reducing memory requirements, suitable for neural network co-processors and embedded hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional image-to-text and text-to-speech models are combined, then image-to-audio conversion can be achieved, but the system becomes complex and computationally expensive
Solution Approach 1:
The patent merges the image-to-text and text-to-speech models into a single integrated image-to-audio conversion model. This unified architecture eliminates the need for separate processing stages and manual programming of multiple models, thereby reducing system complexity while maintaining the full image-to-audio conversion capability through end-to-end neural network processing
2Extent of automation
If conventional multi-stage models are used, then conversion functionality is achieved, but computational cost and power consumption increase
Solution Approach 1:
By combining multiple processing stages into a single integrated neural network model, the patent reduces the total computational operations required. The end-to-end architecture processes images directly to audio waveforms through unified neural layers, eliminating redundant computations that would occur in separate image-to-text and text-to-speech stages, thereby reducing power consumption and computational cost
3Extent of automation
If separate image-to-text and text-to-speech models are used, then conversion can be performed, but issues arise at the interface due to differences in output sequence lengths
Solution Approach 1:
The patent resolves interface compatibility issues by merging the two separate models into one unified architecture. The single end-to-end neural network processes images through intermediate representations directly to audio waveforms, eliminating the interface problems that arise when connecting separate models with mismatched output sequence lengths. The unified model maintains consistent internal representations throughout the processing pipeline
4Extent of automation
If manual programming is required for conventional systems, then functionality can be implemented, but development costs increase
Solution Approach 1:
The patent enables the system to serve itself through automated end-to-end training of the neural network model. The unified architecture allows for automatic learning of optimal transformations from images to audio waveforms through training on labeled data, eliminating the need for manual programming of multiple separate models. This automated approach significantly reduces development costs while maintaining full conversion functionality
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes: An apparatus comprising: at least one memory; instructions; and processor circuitry to execute the instructions to: processor circuitry to execute the instructions to: identify a word in an image, the word to be converted to an audio waveform; encode the word identified in the image into an ordered list of phonemes; and synthesize the audio waveform of the word based on an output of a neural network that determines a duration that a phoneme of the ordered list of phonemes is to be expressed in the audio waveform.