AI Speech Synthesis Pipeline for Low-Latency Prosody Generation
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Solution Overview
Problem
Conventional speech synthesis techniques suffer from significant latency issues, accuracy problems, and an inability to sufficiently capture style and emotion in speech data.
Innovation Solution
Implementing artificial intelligence techniques, including language models, text-to-speech frontend and prosody models, to generate speech data by processing phonetic and prosodic data in sequential portions, and using an LM-TTS adaptor to convert output into phonetic and prosodic information for TTS systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech synthesis techniques are used, then the system is simpler to implement, but latency is significant and accuracy is poor
Solution Approach 1:
The patent segments the speech synthesis system into distinct modular components: a language model component for text generation, a text-to-speech frontend component for phonetic conversion, and a text-to-speech prosody component for intonation control. This segmentation allows each component to be optimized independently while working together to achieve high accuracy without overwhelming complexity
Solution Approach 2:
The patent creates a universal speech synthesis system where a single integrated architecture can handle multiple functions: text generation, phonetic conversion, prosody control, and style/emotion capture. The shared components and unified processing pipeline enable the system to perform diverse speech synthesis tasks accurately while maintaining manageable complexity through multi-functionality
2Speed
If conventional speech synthesis techniques are used, then the system is easier to implement, but latency is significant
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing phonetic representations and prosodic patterns during training. The language model pre-generates text sequences, and the TTS components pre-prepare phonetic and prosodic features, allowing the actual speech synthesis to proceed rapidly without complex real-time computations, thus reducing latency
Solution Approach 2:
The patent ensures continuity of useful action through an integrated pipeline where the language model, text-to-speech frontend, and text-to-speech prosody components operate continuously and cooperatively. The sequential processing of text → phonetics → prosody → speech occurs without interruption or idle time, maintaining continuous productive action that reduces overall processing latency
3Adaptability or versatility
If conventional speech synthesis techniques are used, then the system is simpler, but it cannot sufficiently capture style and emotion
Solution Approach 1:
The patent applies local quality by allowing different components to specialize in specific aspects: the language model focuses on text generation and semantic meaning, the text-to-speech frontend focuses on phonetic accuracy, and the text-to-speech prosody component focuses on style, emotion, and intonation. Each component optimizes its local function while contributing to the overall versatility of the system
Data Source
AI summary
Methods, systems, and computer program products for generating speech data using artificial intelligence techniques are provided herein. A computer-implemented method includes implementing one or more artificial intelligence techniques in connection with one or more speech synthesis tasks; generating, in multiple sequential portions, at least one sequence of data, comprising one or more of phonetic data and prosodic data, by processing at least one previously generated sequence of data using the one or more artificial intelligence techniques; and generating speech data corresponding to at least a portion of the sequence of data by processing the at least a portion of the sequence of data using at least one artificial intelligence-based speech synthesis model.