Speech Annotation Feedback for TTS Pause and Phrasing Errors
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Solution Overview
Problem
Existing speech synthesis systems struggle to accurately predict phrasing and pauses in synthesized speech, leading to suboptimal user experiences, and lack efficient methods for evaluating and improving text-to-speech models using user feedback.
Innovation Solution
A system that utilizes user-provided annotations to identify and categorize errors in synthesized speech, generating annotated data for training and testing text-to-speech models, allowing for targeted improvements in phrasing and pause prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech synthesis methods are used, then the system is simple to implement, but the accuracy of phrasing and pause prediction is poor
Solution Approach 1:
The system implements feedback by collecting user annotations on synthesized speech errors and using this feedback to iteratively improve the TTS model. Users annotate phrasing and pause errors, and this annotated data is fed back into the training process to enhance prediction accuracy.
Solution Approach 2:
The system performs preliminary action by pre-processing user annotations and generating training datasets before model retraining. The annotation processing and data preparation steps are completed in advance to create ready-to-use training data for improving the TTS model.
2Measurement precision
If user feedback annotation is implemented, then model accuracy improves, but the time required for evaluation and training increases
Solution Approach 1:
The system applies partial action by focusing annotation efforts on specific error types (phrasing and pause errors) rather than evaluating all possible speech synthesis aspects. This selective approach reduces the overall time required while still achieving meaningful accuracy improvements.
Solution Approach 2:
The system enables self-service by allowing users to automatically annotate their own feedback on synthesized speech without requiring manual expert analysis. Users can quickly identify and annotate errors in the synthesized output, accelerating the data collection process.
3Reliability
If comprehensive error annotation is performed, then training data quality increases, but the complexity of data processing increases
Solution Approach 1:
The system segments the annotation process into distinct categories (phrasing errors, pause errors, and other speech synthesis errors). This segmentation allows for specialized processing of each error type and simplifies the overall data processing pipeline by handling different error categories separately.
Solution Approach 2:
The system uses an intermediary annotation interface that mediates between user feedback and model training data. This intermediary layer processes and structures raw user annotations into standardized training data formats, reducing the complexity of direct data processing.
Data Source
AI summary
Techniques for evaluating speech synthesis components are described. A system presents speech synthesis audio along with the underlying content (such as text) and allows a user to indicate where an error (such as a pause error) appears in the synthesized speech, along with a category and severity of the error. The system may perform operations to make corrections based on the user inputs. The system may also generate training data and/or a test set of similar annotated data, which may be used to train/retrain and/or evaluate speech synthesis model(s) in an automated fashion.