AI Speech Encoding for Automated Naturalness Evaluation
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Solution Overview
Problem
Existing methods for evaluating synthesized speech quality are inefficient and subjective, relying heavily on human evaluators, which becomes impractical when scaling large datasets.
Innovation Solution
A statistical model trained in two stages using multilingual text and speech data to evaluate synthesized speech quality, incorporating pre-training with unlabeled data and fine-tuning with rated data to predict metrics like naturalness and similarity to human speech.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human evaluators are used to assess synthesized speech quality, then evaluation accuracy and subjectivity control are improved, but productivity and scalability deteriorate
Solution Approach 1:
The patent creates an automated evaluation system that copies human evaluators' expertise by training machine learning models on human-rated speech data. The system learns to replicate human judgment patterns, enabling automated assessment that mirrors human evaluator accuracy while eliminating the need for manual human review of each speech sample.
Solution Approach 2:
The patent replaces the mechanical process of human evaluation with an automated computational system. Machine learning models process speech samples algorithmically, substituting the manual mechanical process of human listening and rating with automated signal processing and pattern recognition, thereby dramatically increasing evaluation throughput.
2Reliability
If human evaluators are used to assess synthesized speech quality, then evaluation reliability is improved, but loss of time and scalability worsen
Solution Approach 1:
The patent performs preliminary action by pre-training evaluation models on extensive datasets of human-rated speech samples before deployment. This pre-training phase captures human evaluation patterns and consistency criteria, allowing the automated system to reliably replicate human judgment without requiring actual human evaluators during the assessment process, thereby eliminating time delays.
Solution Approach 2:
The system copies human evaluation reliability by learning from and replicating human rater patterns. The machine learning models are trained to mimic human judgment consistency, preserving the reliability characteristics of human evaluation while enabling parallel processing of multiple speech samples simultaneously, thus eliminating sequential time constraints.
3Productivity
If automated evaluation systems are implemented, then productivity and scalability are improved, but measurement precision and reliability may deteriorate
Solution Approach 1:
The patent applies preliminary action by extensively training automated evaluation models on large datasets of human-rated speech samples before deployment. This pre-training ensures the automated system learns accurate human judgment patterns, maintaining measurement precision while enabling high-throughput evaluation of numerous speech samples without human intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where the automated evaluation models are continuously trained and refined using human rating data. This feedback loop allows the system to learn from and correct its own evaluations, progressively improving measurement precision while maintaining high productivity through automated processing of evaluation results.
4Measurement precision
If multi-stage training with large datasets is used, then model accuracy and adaptability are improved, but device complexity and training time increase
Solution Approach 1:
The patent segments the training process into distinct stages: pre-training on large unlabeled datasets to learn general speech patterns, fine-tuning on smaller labeled datasets for specific evaluation tasks, and continuous refinement through feedback. This segmentation manages system complexity by breaking down the complex training process into manageable phases, each with specific objectives and data requirements.
Solution Approach 2:
The system performs preliminary pre-training on extensive unlabeled speech data before fine-tuning on smaller labeled datasets. This preliminary action allows the model to learn robust general speech representations first, reducing the complexity of subsequent fine-tuning and enabling accurate evaluation with relatively simpler training procedures in later stages.
Data Source
AI summary
A method for evaluating synthesized speech, including receiving a speech sample in a first language; and determining a rating of the speech sample based on an encoding of the speech sample by an artificial intelligence encoding model, the rating of the speech sample corresponding to a naturalness of the speech sample, wherein the encoding of the speech sample is based on a first training stage of the encoding model using a first set of training data and a second training stage of the encoding model using a second set of training data, the first set of training data includes unlabeled speech audio, unlabeled text, and paired speech audio and text data in the first language and at least one additional language, and the second set of training data includes rated speech audio.