Text-to-Speech Synthesis Model with Variance Adaptor
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Solution Overview
Problem
Conventional deep learning-based text-to-speech synthesis methods require high-quality 'text-voice' data pairs for training, leading to long training and inference times, inability to perform parallel processing, and generate unnatural speech lacking emotional expression.
Innovation Solution
A text-to-speech synthesis model that integrates speaker and emotion control in real-time using a framework combining phoneme embeddings, CNN-based transformer encoders and decoders, and a variance adaptor to adapt vector representations for pitch, energy, and duration, with a vocoder for natural speech generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep learning-based speech synthesis methods are used to generate high-quality speech, then speech quality is improved, but training time and inference time increase significantly
Solution Approach 1:
The model segments the speech synthesis process into separate encoder and decoder components that can process different aspects of speech generation independently. The encoder processes text input to extract semantic features, while the decoder generates speech waveforms, allowing for optimized processing of each stage and enabling parallel computation paths.
Solution Approach 2:
The system dynamically adapts the processing pipeline based on input requirements, using conditional computation paths that activate different model components only when needed. This dynamic architecture allows the model to maintain high speech quality while reducing computation time for simpler synthesis tasks by skipping certain processing stages.
2Productivity
If conventional deep learning models are used for speech synthesis, then speech generation capability is achieved, but parallel processing capability is lost
Solution Approach 1:
The architecture divides the synthesis task into independently processable segments including text encoding, feature extraction, and waveform generation stages. Each segment can be computed in parallel across multiple processing units, with well-defined interfaces between stages that maintain data flow integrity while enabling massive parallelization.
Solution Approach 2:
The model employs universal processing blocks that can handle multiple types of operations within a single computational framework. These multi-functional blocks process different aspects of speech synthesis (semantic encoding, acoustic feature generation, waveform prediction) using the same underlying architecture, allowing unified parallel processing across all synthesis tasks.
3Adaptability or versatility
If basic text-to-speech models are used, then speech generation is achieved, but natural speech expression and emotional variation are lost
Solution Approach 1:
The system adds emotional and stylistic dimensions to the basic text-to-speech mapping by introducing separate embedding layers for emotion tags and speaker identifiers. These additional dimensional inputs are integrated with the text semantic features in the encoder, creating a multi-dimensional feature space that enables natural speech expression and emotional variation without fundamentally changing the core synthesis architecture.
Solution Approach 2:
The model performs preliminary processing of emotional and stylistic attributes by pre-computing emotion embeddings and speaker features from input tags before the main synthesis process. This preliminary action prepares the contextual information in advance, allowing the decoder to directly apply these pre-computed features during waveform generation without adding computational complexity to the critical synthesis path.
Data Source
AI summary
Embodiments described herein provide systems and methods for text to speech synthesis. A system receives, via a data interface, an input text, a reference spectrogram, and at least one of an emotion ID or speaker ID. The system generates, via a first encoder, a vector representation of the input text. The system generates, via a second encoder, a vector representation of the reference spectrogram. The system generates, via a variance adaptor, a modified vector representation based on a combined representation including a combination of the vector representation of the input text, the vector representation of the reference spectrogram, and at least one of an embedding of the emotion ID or an embedding of the speaker ID. The system generates, via a decoder, an audio waveform based on the modified vector representation. The generated audio waveform may be played via a speaker.