Multimodal Speech Generation Training for Emotional Timbre Realism
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Solution Overview
Problem
Conventional speech cloning techniques fail to seamlessly integrate emotional and timbre aspects of speech generation, making artificially intelligent speech distinguishable from real human speech.
Innovation Solution
A multi-modal speech generation model trained using image, audio, and text information to predict phoneme duration, pitch contour, sound volume, and acoustic spectrum, with loss functions optimized to enhance realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional speech cloning techniques are used to generate speech with reference timbre, then the speech can be generated with desired timbre, but the generated speech becomes easily distinguishable from real human speech due to lack of emotional expression
Solution Approach 1:
The speech generation system is divided into multiple independent sub-models, each responsible for generating specific acoustic features (phoneme duration, pitch contour, sound volume, acoustic spectrum). This segmentation allows each sub-model to be optimized separately while contributing to the overall realism of the generated speech, resolving the contradiction between realism and system complexity.
Solution Approach 2:
The speech generation model integrates multiple functions into a unified system that can generate various acoustic features simultaneously from the same input (text and reference speech). This multi-functional approach enables the system to produce emotionally expressive speech with realistic timbre without requiring separate specialized systems for each feature.
2Reliability
If multi-modal information (image, audio, text) is used to train the speech generation model, then the speech can seamlessly integrate emotional and timbre attributes, but the training process becomes more complex
Solution Approach 1:
The training process is segmented into multiple stages with separate loss functions for each sub-model (phoneme duration loss, pitch contour loss, sound volume loss, acoustic spectrum loss). This segmentation makes the complex multi-modal training process more manageable and systematic, allowing progressive optimization of each feature while maintaining overall realism.
Solution Approach 2:
The system uses different loss functions with varying weights to balance the importance of different acoustic features during training. By adjusting these parameters, the system can optimize the integration of multi-modal information (image, audio, text) to achieve realistic speech while managing training complexity through parameter tuning rather than process complexity.
Data Source
AI summary
A method in an illustrative embodiment includes determining a first loss function for a first sub-model of a speech generation model based on a plurality of feature vectors associated with training image information, training audio information, and training text information used to train the speech generation model. The method may further include determining a second loss function for a second sub-model and a third loss function for a third sub-model of the speech generation model based on the plurality of feature vectors that have been processed. In addition, the method may further include determining a fourth loss function for a fourth sub-model of the speech generation model based on the processed plurality of feature vectors. The method may further include updating parameters of the speech generation model based on the first loss function, the second loss function, the third loss function, and the fourth loss function.