Prosody Prediction for Expressive Text-to-Speech Synthesis
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Solution Overview
Problem
Existing speech processing systems struggle to generate synthesized speech that reflects the expressiveness of human speech, particularly in long-form reading and dialogues, leading to monotonous and robotic output that can be difficult to follow.
Innovation Solution
A TTS system incorporating a prosody prediction model trained with a linguistic encoder and audio encoder to generate acoustic and linguistic representations in a shared space, using a large autoregressive transformer to predict prosodic variations based on text and audio data, including contextualized word embeddings and speaker embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional TTS systems are used to generate synthesized speech, then the speech output is clear and intelligible, but the speech lacks expressiveness and sounds monotonous and robotic
Solution Approach 1:
The system changes multiple parameters simultaneously including pitch contours, speech rate variations, pause durations, and stress patterns to generate prosodic features. These parameter variations are predicted by neural network models that analyze linguistic and acoustic representations, enabling the synthesized speech to exhibit natural human-like expressiveness while maintaining intelligibility.
2Object-generated harmful factors
If prosodic variations are added to improve expressiveness, then the speech becomes more natural and engaging, but the system complexity increases significantly
Solution Approach 1:
The system segments the prosody generation process into distinct functional modules: a linguistic encoder that processes text to extract linguistic features, an audio encoder that processes reference audio to extract acoustic features, and a prosody prediction model that integrates these features to predict prosodic parameters. This segmentation allows each component to be optimized independently while working together to produce natural-sounding speech.
Solution Approach 2:
The system introduces intermediate representations including linguistic embeddings that capture semantic and syntactic information, acoustic embeddings that capture phonetic and prosodic information, and predicted prosodic features that serve as mediators between the encoders and the final speech synthesis. These intermediaries enable complex prosodic variations to be generated through a structured, manageable process.
3Productivity
If long-form content is synthesized without prosodic variations, then the processing is simple and fast, but the speech becomes difficult to follow and loses listener engagement
Solution Approach 1:
The system performs preliminary analysis of the input text to identify linguistic features such as sentence boundaries, clause structures, and semantic relationships before generating prosodic variations. The linguistic encoder pre-processes the text to extract meaningful patterns that will guide prosody prediction, enabling the system to apply appropriate expressiveness automatically without real-time complex decision-making, thus maintaining processing efficiency while improving engagement.
Data Source
AI summary
A speech-processing system may be configured to generate expressive synthesized speech. The system may include a prosody prediction model that generates a combination of durations and acoustic representations that may be based on the content of the text as well as additional context information. The model may be trained to predict a joint probability between linguistic representations (e.g., derived from text) and combined duration/acoustic representations (e.g., derived from audio). At inference, the model can process linguistic representations derived from text to predict combined duration/acoustic representations. In some implementations, the model may process additional information; for example, semantic embeddings output by a language model based on the text. In another example, the model may receive a speaker embedding representing voice characteristics of a particular speaker. A decoder may process the durations and acoustic representations output by the model to generate audio data representing the synthesized speech and representing expressive prosodic variation.