Parallel Conv-RNN Streaming TTS for Low-Latency Speech Quality
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Solution Overview
Problem
Existing TTS systems face challenges in latency and quality, with conventional systems failing to balance latency reduction and quality, with conventional systems failing to balance latency reduction and quality, with conventional systems failing to balance latency and quality.
Innovation Solution
The use of parallel convolutional recurrent neural networks (ParConvRNN) in a parallel architecture to generate synthesized speech from text, reducing latency and maintaining quality by combining convolutional and recurrent neural networks in a parallel structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sequential network layers are used in TTS systems, then the processing pipeline is complete and functional, but latency increases and processing time extends
Solution Approach 1:
The TTS processing pipeline is segmented into independent parallel branches: a convolutional neural network branch for local feature extraction and a recurrent neural network branch for sequential processing. These branches operate simultaneously on the same input features, dividing the原本 sequential workload into parallel tasks that can execute concurrently, thereby reducing overall latency while maintaining complete processing functionality
Solution Approach 2:
The system transitions from a single-dimensional sequential processing architecture to a multi-dimensional parallel architecture by adding the convolutional processing dimension alongside the existing recurrent processing dimension. This dimensional expansion allows both processing types to occur simultaneously in different computational spaces, reducing the time dimension (latency) while preserving all necessary processing steps
2Loss of time
If processing layers are reduced to decrease latency, then TTS processing time decreases, but speech synthesis quality degrades
Solution Approach 1:
The system merges convolutional neural network layers with recurrent neural network layers into a unified parallel architecture where both processing types occur simultaneously. This combination allows the system to maintain comprehensive processing capabilities (preserving speech quality) while executing operations in parallel rather than sequentially, thereby reducing overall processing time without sacrificing synthesis quality
Solution Approach 2:
The TTS system employs a composite neural network architecture combining convolutional and recurrent neural network components. This composite structure leverages the strengths of both architectures: convolutional layers for efficient local pattern recognition and recurrent layers for sequential dependency modeling. The composite design enables complete processing functionality with reduced latency, maintaining speech quality while decreasing processing time
3Productivity
If convolutional and recurrent neural networks are combined in parallel architecture, then latency is reduced and processing efficiency improves, but system complexity increases
Solution Approach 1:
The parallel architecture is segmented into distinct convolutional and recurrent processing branches with clearly defined interfaces. Each branch processes features independently and outputs to a common synthesis stage, modularizing the complexity and making the system more manageable despite the increased parallel processing capabilities
Solution Approach 2:
The parallel convolutional-recurrent architecture serves multiple functions simultaneously: it performs both local feature extraction and sequential processing, handles multiple speech characteristics in parallel, and maintains compatibility with existing TTS pipelines. This multi-functionality justifies the increased complexity by delivering superior processing efficiency and versatility
Data Source
AI summary
Systems and methods are provided for generating a lightweight, high-quality streaming text-to-speech (TTS) system. For example, some disclosed systems are configured to obtain a first model comprising one or more layers of a convolutional neural network. Each layer of the convolutional neural network is configured to generate a new output from a previous input. The systems also obtain a second model comprising a recurrent neural network. Subsequent to obtaining the first model and the second model, the systems are configured to compile the one or more layers of the convolutional neural network and the recurrent neural network in a parallel architecture to generate a machine learning module such that each model of the machine learning module is configured to receive input simultaneously.