Style Embedding TTS Voice Response for Assistant Systems
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Solution Overview
Problem
Current smart assistant systems lack the ability to provide natural and context-aware interactions, as they often rely on monotonic machine-generated speech that fails to mimic the tone, speed, and volume of user input, leading to user fatigue and reduced trust.
Innovation Solution
The system employs a multi-modal style classification model to extract style embeddings from user input, allowing for the generation of customized text-to-speech responses that match the user's speech style, using a semi-supervised approach combining the interactive emotional dyadic motion capture database with a small labeled internal dataset for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If monotonic machine-generated speech is used for assistant responses, then system simplicity is maintained, but user engagement and natural interaction quality deteriorate
Solution Approach 1:
The system extracts style parameters (tone, speed, volume, pitch) from user speech and applies these parameters to the TTS synthesis process. This allows the assistant to dynamically adjust speech characteristics to match user style, transforming monotonic machine speech into natural, engaging conversation while maintaining system manageability through parameter-based control.
Solution Approach 2:
The patent introduces style embeddings as an intermediary representation that bridges user speech input and TTS output. The style classification model extracts style features from user input and converts them into embeddings that guide the TTS synthesis, acting as a mediator that translates user speech characteristics into synthesized speech parameters without requiring complex end-to-end modeling.
2Reliability
If speech style matching is implemented, then interaction naturalness is improved, but computational resources and processing time increase
Solution Approach 1:
The system focuses on extracting and matching key style parameters (tone, speed, volume, pitch) rather than attempting to replicate all aspects of user speech. This partial action approach achieves sufficient naturalness for effective communication while avoiding the excessive computational cost of full speech cloning or complete acoustic feature replication.
Solution Approach 2:
The style classification model is pre-trained on large datasets to efficiently extract style embeddings from user speech. This preliminary training allows the model to quickly identify and extract relevant style parameters during interaction, reducing real-time computational requirements while maintaining high interaction naturalness.
3Adaptability or versatility
If style embeddings are extracted from user input, then speech customization capability is enhanced, but system complexity and training data requirements increase
Solution Approach 1:
The patent separates the speech processing system into distinct modules: a style classification model for extracting style parameters, an embedding generation component for converting parameters to embeddings, and a TTS synthesis module for generating speech. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high customization capability.
Solution Approach 2:
The style classification model is designed to extract multiple style parameters (tone, speed, volume, pitch) simultaneously from user input, and the embedding representation serves multiple functions in guiding TTS synthesis. This multi-functionality approach achieves comprehensive speech customization without requiring separate specialized components for each parameter.
Data Source
AI summary
In one embodiment, a method includes receiving a voice input having first audio features at a client system, generating a text response corresponding to the voice input, wherein the text response is associated with style features, generating an output audio waveform of the text response by a text-to-speech model on the client system, wherein the output audio waveform is generated based on the first audio features and the style features, wherein the output audio waveform comprises second audio features, and rendering the output audio waveform at the client system in response to the voice input.