Speech Processing System Variational Autoencoder Prosody
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Solution Overview
Problem
Current speech-processing systems struggle to generate synthesized speech that accurately captures variations in prosody, such as speech rate and pitch, making the output sound unnatural compared to human speech.
Innovation Solution
A speech-processing system employing a variational autoencoder (VAE) is trained using altered training data to learn the spectrum of prosodic variations, allowing it to generate synthesized speech that includes a range of prosodic characteristics, thereby improving the naturalness of the output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional speech synthesis methods are used, then the system is simple to implement, but the synthesized speech lacks natural prosodic variations
Solution Approach 1:
The system pre-processes training audio data to extract prosodic features and generates altered training data with varied prosodic characteristics before training the VAE model. This preliminary preparation enables the model to learn prosodic variations more effectively, improving synthesized speech naturalness without requiring complex real-time processing during inference
Solution Approach 2:
The variational autoencoder model dynamically generates prosodic variations by sampling from a learned probability distribution. Instead of using fixed prosodic patterns, the system introduces randomness and adaptability in prosody generation, allowing the synthesized speech to exhibit natural variations in speech rate, pitch, and emphasis that match human speech characteristics
2Reliability
If the system learns prosodic variations from training data, then the synthesized speech becomes more natural, but the training process becomes more complex
Solution Approach 1:
The system replaces manual prosody adjustment and traditional rule-based prosody generation with a data-driven variational autoencoder model. The VAE automatically learns prosodic patterns from training audio data and generates natural prosodic variations, eliminating the need for complex manual tuning and rule-based systems while improving speech naturalness
Data Source
AI summary
A speech-processing system receives input data representing text. A first encoder processes segments of the text to determine embedding data representing the text, and a second encoder processes corresponding audio data to determine prosodic data corresponding to the text. The embedding and prosodic data is processed to create output data including a representation of speech corresponding to the text and prosody.