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concept#Artificial Intelligence#Platform#Integration#Product

Text-to-Speech

Text-to-Speech (TTS) describes automatic generation of spoken audio from text, often using neural models. It covers quality, prosody, latency, privacy and integration aspects.

Text-to-Speech (TTS) refers to automatic generation of spoken audio from text using rule-based or neural synthesis methods.
Established
High

Classification

  • High
  • Technical
  • Technical
  • Intermediate

Technical context

Web Speech API / browser integrationCloud TTS APIs (e.g., Google, AWS) for scalable operationOpen-source engines (e.g., Coqui TTS) for on-premise operation

Principles & goals

Clear separation between text preprocessing, model selection and output layerRespect privacy and data minimization for voice and training dataQuality over features: prioritize prosody and intelligibility
Build
Domain, Team

Use cases & scenarios

Compromises

  • Misuse for deepfake audio and identity fraud
  • Violation of personal and copyright rights for voices
  • Insufficient privacy measures at cloud providers
  • Use SSML consistently to control prosody
  • Run A/B tests to choose voices and parameters
  • Ensure privacy via anonymization and minimal storage

I/O & resources

  • Raw text or structured content (SSML)
  • Voice profiles and configuration data
  • System requirements for latency, accessibility and privacy
  • Streaming audio (OPUS, PCM) or audio files (MP3, WAV)
  • Diagnostic logs and metrics
  • Metadata about voice, language and synthesis parameters

Description

Text-to-Speech (TTS) refers to automatic generation of spoken audio from text using rule-based or neural synthesis methods. The concept covers quality, prosody, latency, privacy and integration requirements within product and platform architectures. It focuses on architectural choices, operational models and ethical considerations.

✔

  • Improved accessibility for users with impairments
  • Automated audio generation reduces production effort
  • Scalable delivery of voice interfaces

✖

  • Natural intonation and emotion remain limited
  • Voice quality varies widely by model and language
  • High compute cost for real-time neural models

  • Words-per-second (WPS)

    Measures output rate; important for latency assessment.

  • MOS (Mean Opinion Score)

    Subjective quality rating from user studies.

  • Pronunciation error rate

    Share of incorrectly generated or unintelligible outputs.

Read-aloud feature for news app

Integration of a TTS engine to read articles aloud to users; focus on voice quality and offline caching.

IVR voices for customer support

Cloud TTS provides dynamic prompts in multiple languages, including GDPR-compliant data handling.

Accessible learning platform

Automatic audio versions of learning materials increase accessibility for visually impaired users.

1

Requirements analysis (quality, latency, privacy)

2

Select engine (cloud vs. on-premise) and voice

3

Integrate, test (MOS, intelligibility) and monitor

⚠️ Technical debt & bottlenecks

  • Outdated models without deprecation plan
  • Missing infrastructure for efficient scaling
  • Incomplete test data for edge cases and dialects
Compute capacity for neural modelsNetwork bandwidth for streamingQuality of text preprocessing (normalization)
  • Creating deceptively realistic voices without consent
  • Uncritical use of low-quality voices in critical systems
  • Sharing sensitive text with non-GDPR-compliant services
  • Underestimating the importance of text normalization
  • Lack of performance tests for real user load
  • Ignoring legal risks in voice synthesis
Basics of speech signal processingML/AI knowledge for neural synthesisDevOps experience for deployment and monitoring
Latency requirements for real-time interactionPrivacy and data localizationScalability and cost control
  • • Language support limited per model
  • • Legal restrictions for voice licenses
  • • Costs at high throughput or low latency