Position-Based Text-to-Speech Model for Semi-Structured Documents
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Solution Overview
Problem
Conventional text-to-speech systems face inaccuracies when dealing with semi-structured documents, as they fail to correctly infer the document structure, leading to unintelligible output due to mismatches in headers, cell sizes, and other layout issues.
Innovation Solution
A position-based text-to-speech model is introduced, utilizing an encoder-decoder architecture that learns to reorder text and generate spectrograms simultaneously, leveraging document positional encodings to determine the correct reading order and improve audio/text alignment, reducing errors and unnatural pauses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional text-to-speech systems process text in linear sequence, then processing is simple, but accuracy deteriorates when dealing with semi-structured documents having complex layouts
Solution Approach 1:
The patent introduces positional encoding that adds spatial dimension information to the text processing. Instead of treating text as a simple linear sequence, the model incorporates 2D positional coordinates (row and column positions) from the document layout, transforming the 1D text processing into a 2D spatial-aware processing framework. This allows the model to understand document structure and correctly reorder text according to visual layout rather than linear sequence.
Solution Approach 2:
The patent segments the text processing by introducing positional encoding vectors that separate the textual content information from the spatial position information. Each text token is augmented with its positional coordinates, creating independent position embeddings that can be processed separately and then combined. This segmentation allows the model to handle complex layouts by treating position as a distinct dimension rather than conflating it with text sequence.
2Reliability
If the model reorders text based on document structure, then speech intelligibility improves, but processing time increases
Solution Approach 1:
The patent performs preliminary action by computing positional encodings for all text tokens in parallel during the encoding phase, before the actual text reordering and speech generation. The positional information is pre-integrated into the token representations, allowing the model to make reordering decisions based on already-computed position vectors rather than performing additional sequential analysis during generation. This preliminary computation of spatial relationships accelerates the overall processing.
Solution Approach 2:
The patent replaces mechanical sequential text reordering with a learned attention-based reordering mechanism. Instead of physically moving text tokens based on explicit layout rules, the model uses self-attention mechanisms to dynamically weight and reorder tokens based on their positional encodings. This substitution of mechanical reordering with neural attention computation enables more efficient parallel processing while maintaining speech intelligibility.
3Measurement precision
If positional encodings are added to text tokens, then document structure understanding improves, but computational load increases
Solution Approach 1:
The patent applies local quality by using relative positional encodings that focus computational resources on local spatial relationships rather than global position computation. The positional encoding vectors are designed to capture relative position information (distance and direction between tokens) rather than absolute coordinates, allowing the model to understand document structure with reduced computational overhead. This local focus enables efficient processing of spatial relationships without computing full global position matrices.
Data Source
AI summary
Position-based text-to-speech model and training techniques are described. A digital document, for instance, is received by an audio synthesis service. A text-to-speech model is utilized by the audio synthesis service to generate digital audio from text included in the digital document. The text-to-speech model, for instance, is configured to generate a text encoding and a document positional encoding from an initial text sequence of the digital document. The document positional encoding is based on a location of the text encoding within the digital document. Digital audio is then generated by the text-to-speech model that includes a spectrogram having a reordered text sequence, which is different from the initial text sequence, by decoding the text encoding and the document positional encoding.