Autoencoder Text-to-Speech Synthesis Reducing Computational Load
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Solution Overview
Problem
Conventional text-to-speech synthesis methods face challenges in efficiently generating natural-sounding speech due to high computational requirements and limited ability to represent variable-length audio units effectively, leading to suboptimal quality and increased latency.
Innovation Solution
A text-to-speech system utilizing an autoencoder network with a linguistic encoder and an acoustic encoder, both incorporating long short-term memory layers, to generate fixed-size speech unit representations that can be used for unit-selection synthesis, reducing computational complexity and improving output quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional deep neural network based speech synthesis is used with frame-level acoustic representation, then speech quality can be improved, but computational requirements increase significantly
Solution Approach 1:
The patent extracts the essential acoustic characteristics of speech units and represents them using fixed-dimensional bottleneck features. By taking out only the most important acoustic properties and encoding them in a compact fixed-size representation, the system maintains speech quality while significantly reducing computational requirements compared to full frame-level representations.
Solution Approach 2:
The patent changes the parameter representation from variable-length frame-level acoustic features to fixed-dimensional bottleneck features. This parameter transformation allows the system to capture essential speech characteristics in a compact form, reducing computational complexity while preserving speech quality through the use of autoencoder-based dimensionality reduction.
2Productivity
If variable-duration speech units are mapped to fixed-dimensional representations, then computational efficiency is improved, but the ability to represent acoustic characteristics accurately may be degraded
Solution Approach 1:
The patent introduces an autoencoder-based bottleneck representation as an intermediary between variable-duration speech units and fixed-dimensional vectors. This intermediary structure learns to compress variable-length acoustic sequences into fixed-size representations that preserve essential speech characteristics, enabling both computational efficiency and accurate acoustic representation.
Solution Approach 2:
The patent transforms variable-duration speech units into fixed-dimensional representations by projecting them into a different dimensional space using autoencoder bottlenecks. This dimensional transformation allows the system to handle speech units of varying lengths uniformly while maintaining acoustic fidelity through the learned compression in the bottleneck layer.
3Measurement precision
If text features are repeated for the entire duration of phoneme mapping, then mapping accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential linguistic and acoustic features needed for speech synthesis and represents them in fixed-dimensional form. By extracting and representing only the critical features rather than repeating full text features throughout phoneme duration, the system maintains mapping accuracy while significantly reducing processing time.
Data Source
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AI summary
Methods, systems, and computer-readable media for text-to-speech synthesis using an autoencoder. In some implementations, data indicating a text for text-to-speech synthesis is obtained. Data indicating a linguistic unit of the text is provided as input to an encoder. The encoder is configured to output speech unit representations indicative of acoustic characteristics based on linguistic information. A speech unit representation that the encoder outputs is received. A speech unit is selected to represent the linguistic unit, the speech unit being selected from among a collection of speech units based on the speech unit representation output by the encoder. Audio data for a synthesized utterance of the text that includes the selected speech unit is provided.