Emotion-Based Text To Speech Embedding Generation
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Solution Overview
Problem
Current social network systems struggle to generate human-like speech output with emotions for text-to-speech (TTS) functionality, leading to a cold and impassive experience, which is inefficient and resource-intensive as users often have to record their own voices for videos.
Innovation Solution
A TTS system that dynamically generates audio files by selecting a speaker and generating embeddings for emotions not initially available, allowing for the creation of new embeddings based on existing voice and style components of other speakers, enabling more realistic and emotionally expressive speech.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional text-to-speech systems are used to generate speech output, then the system is simple to operate, but the speech lacks emotional expression and sounds cold and impassive
Solution Approach 1:
The speech generation system is segmented into multiple independent components: voice embeddings module, style embeddings module, and acoustic generator. This allows separate processing and optimization of voice characteristics and emotional expression, enabling realistic speech generation while maintaining system operational simplicity through modular design
Solution Approach 2:
Style embeddings are introduced as an intermediary element that bridges the gap between text input and speech output. These embeddings capture emotional characteristics and are integrated into the acoustic generation process, enabling emotional expression without requiring users to manually record audio
2Reliability
If users record their own voices for videos to achieve emotional expression, then the speech becomes more realistic and emotionally expressive, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
Voice embeddings and style embeddings are pre-computed and stored in databases before actual speech generation. This preliminary preparation allows the system to quickly assemble realistic emotional speech by retrieving and combining pre-processed embedding data, eliminating the need for time-consuming user recording sessions
Solution Approach 2:
The system creates synthetic voice copies through embedding representations rather than requiring actual user recordings. By generating speech that mimics human emotional expression through learned patterns in the embeddings, the system achieves realism without the time and resource costs of capturing actual user audio
3Reliability
If users record their own voices for videos to achieve emotional expression, then the speech becomes more realistic and emotionally expressive, but system resources are wasted
Solution Approach 1:
The embedding-based speech generation system serves multiple functions: it generates emotionally expressive speech, supports multiple languages and accents through diverse embeddings, and can be applied across different video and audio content types. This multi-functionality eliminates the need for separate user recording sessions for each application, reducing overall system resource consumption
Solution Approach 2:
The system achieves different speech characteristics by changing embedding parameters (voice type, style, emotion) rather than requiring separate physical recordings for each scenario. This parameter-based control allows efficient generation of diverse realistic speech outputs without proportionally increasing resource usage
Data Source
AI summary
Systems and methods are provided for providing emotion-based text to speech. The systems and methods perform operations comprising accessing a text string; storing a plurality of embeddings associated with a plurality of speakers, a first embedding for a first speaker being associated with a first emotion and a second embedding for a second speaker of the plurality of speakers being associated with a second emotion; selecting the first speaker to speak one or more words of the text string; determining that the one or more words are associated with the second emotion; generating, based on the first embedding and the second embedding, a third embedding for the first speaker associated with the second emotion; and applying the third embedding and the text string to a vocoder to generate an audio stream comprising the one or more words being spoken by the first speaker with the second emotion.