Cloud TTS Service Hides Backend Processing via API
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Solution Overview
Problem
Current text-to-speech (TTS) systems require collaboration between parties with proprietary speech data and algorithms, leading to intellectual property disclosure and friction, which hinders the spread of TTS benefits, especially in scenarios where parties are at arm's length.
Innovation Solution
A divided client-server approach using API calls to generate TTS voices, where the server processes speech samples, transcriptions, and metadata without revealing internal operations, allowing collaboration without disclosing sensitive intellectual property, and enabling language-agnostic voice creation with iterative improvement of speech samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If parties collaborate closely to generate TTS voices, then TTS quality and effectiveness improve, but intellectual property disclosure increases and friction increases
Solution Approach 1:
The TTS system is divided into separate modules (speech data recording, TTS algorithm processing, voice generation) that can be independently developed and owned by different parties. Each party maintains control over their specific module while collaborating through standardized interfaces, thus improving TTS quality through specialization without requiring full IP disclosure.
Solution Approach 2:
A cloud-based TTS service platform acts as an intermediary between parties with speech data and parties with TTS algorithms. The platform provides standardized API interfaces that enable collaboration without direct exposure of proprietary information, allowing high-quality voice generation while maintaining intellectual property boundaries.
2Manufacturing precision
If parties collaborate closely to generate TTS voices, then TTS quality and effectiveness improve, but friction between parties increases
Solution Approach 1:
The cloud-based TTS platform provides universal access to voice generation capabilities through standardized APIs that work with different speech data and algorithm combinations. This multi-functional approach allows various parties to collaborate without custom integration work, reducing friction while maintaining high TTS quality through the platform's coordinated processing.
3Reliability
If proprietary TTS algorithms and speech data are kept separate, then intellectual property protection improves, but collaboration efficiency decreases
Solution Approach 1:
The cloud-based TTS service platform serves as an intermediary that receives speech data from one party, processes it through TTS algorithms from another party, and generates voices without requiring direct collaboration between the proprietary owners. This maintains strong IP protection while achieving high collaboration efficiency through automated platform-mediated processing.
4Adaptability or versatility
If comprehensive speech data is collected for all languages and purposes, then TTS coverage and quality improve, but data collection time and redundancy increase
Solution Approach 1:
The system performs preliminary analysis of speech data to identify coverage holes before full data collection begins. By预先 identifying which languages, accents, or purposes are underrepresented, the system can target data collection efforts efficiently, improving TTS coverage without unnecessary time spent on redundant data gathering.
Solution Approach 2:
The system iteratively analyzes generated TTS voices against target coverage goals and provides feedback on what additional speech data is needed. This feedback-driven approach allows the system to progressively improve TTS coverage for different languages and purposes while minimizing redundant data collection by focusing only on identified gaps.
Data Source
AI summary
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for generating speech. One variation of the method is from a server side, and another variation of the method is from a client side. The server side method, as implemented by a network-based automatic speech processing system, includes first receiving, from a network client independent of knowledge of internal operations of the system, a request to generate a text-to-speech voice. The request can include speech samples, transcriptions of the speech samples, and metadata describing the speech samples. The system extracts sound units from the speech samples based on the transcriptions and generates an interactive demonstration of the text-to-speech voice based on the sound units, the transcriptions, and the metadata, wherein the interactive demonstration hides a back end processing implementation from the network client. The system provides access to the interactive demonstration to the network client.