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Speech & Audio AI

What is Text-to-Speech (TTS)?

Text-to-Speech (TTS) is an AI technology that converts written text into natural-sounding spoken audio. Modern TTS systems use deep learning to produce voices that closely mimic human speech patterns, intonation, and emotion, enabling applications from customer service automation to accessibility tools and content creation.

What is Text-to-Speech (TTS)?

Text-to-Speech, or TTS, is the technology that enables computers to read text aloud in a natural-sounding human voice. When your navigation app gives you turn-by-turn directions, when a voice assistant reads your messages, or when an automated phone system speaks to you, TTS is the technology generating that spoken output.

Modern TTS has moved far beyond the robotic, monotone voices of early systems. Today's neural TTS engines produce speech that is remarkably human-like, with natural intonation, appropriate pauses, emotional nuance, and even breathing sounds. In many cases, listeners cannot distinguish AI-generated speech from recordings of real human speakers.

How TTS Works

Contemporary TTS systems typically operate in two main stages:

  • Text analysis: The system processes the input text to determine how it should be spoken. This includes identifying sentence boundaries, resolving abbreviations, determining the pronunciation of numbers and dates, and applying appropriate stress and intonation patterns based on context.
  • Speech synthesis: The processed text is converted into audio waveforms. Modern neural TTS systems use deep learning models trained on hundreds of hours of recorded human speech to generate audio that captures the natural rhythm, pitch, and quality of human voices.

The most advanced TTS systems use architectures like neural vocoders and transformer-based models that can generate speech in real time, adjusting speaking style, speed, pitch, and emotional tone based on the content and context.

Key Approaches to TTS

  • Concatenative synthesis: The oldest approach, stitching together pre-recorded speech fragments. Still used in some limited-vocabulary applications but sounds unnatural for general text.
  • Parametric synthesis: Uses mathematical models to generate speech signals. More flexible than concatenative but historically less natural-sounding.
  • Neural TTS: The current standard, using deep neural networks to generate highly natural speech. Models like Tacotron, WaveNet, and VITS have revolutionised TTS quality.

Business Applications of TTS

TTS has become essential infrastructure for businesses seeking to communicate with customers and employees through voice:

Customer Service Automation

  • Interactive voice response (IVR) systems that guide callers through menu options and provide information without human agents
  • Voice-enabled chatbots that can handle routine enquiries by speaking naturally to customers
  • Automated outbound calls for appointment reminders, delivery notifications, and payment confirmations

Accessibility

  • Making digital content accessible to visually impaired users
  • Reading aloud documents, emails, and web content for users with reading difficulties
  • Providing audio alternatives for text-heavy applications and interfaces

Content Creation and Media

  • Generating voice-overs for training videos, tutorials, and e-learning modules without hiring voice actors
  • Converting written articles and reports into podcast-style audio content
  • Producing audio versions of product catalogues and marketing materials

Internal Operations

  • Audio alerts and notifications in warehouse, manufacturing, and logistics environments where workers cannot easily read screens
  • Voice-guided workflows for field technicians performing maintenance or inspections
  • Automated reading of reports and dashboards during commutes

TTS in Southeast Asia

TTS presents unique opportunities and considerations in the ASEAN context:

  • Multilingual customer engagement: Businesses serving customers across multiple ASEAN markets can use TTS to deliver voice communications in Bahasa Indonesia, Thai, Vietnamese, Tagalog, and other regional languages without maintaining separate voice talent pools for each market.
  • Rising mobile-first populations: Across Southeast Asia, many consumers prefer voice interactions over text, particularly in markets where smartphone literacy outpaces text literacy. TTS enables businesses to serve these customers through voice-first interfaces.
  • Local language quality: TTS quality varies significantly across languages. English and Mandarin TTS is highly mature, while smaller ASEAN languages may have less natural-sounding synthesis. However, quality is improving rapidly as more training data becomes available.
  • Cultural sensitivity: Voice characteristics including gender, accent, and speaking style carry cultural implications. Businesses must choose TTS voices that are appropriate and trustworthy for their target market.

Common Misconceptions

"TTS voices all sound robotic." This was true a decade ago but is no longer the case. Neural TTS systems from providers like Google, Amazon, Microsoft, and ElevenLabs produce speech that is often indistinguishable from human recordings in controlled tests.

"TTS is only useful for accessibility." While accessibility remains an important use case, TTS has become a core business tool for customer communication, content creation, and operational efficiency. The global TTS market is projected to exceed USD 7 billion by 2028.

"Creating a custom TTS voice requires massive investment." Modern voice cloning and custom TTS services can create a branded voice from as little as 30 minutes of recorded speech, with costs starting from a few thousand dollars rather than the hundreds of thousands required by older technologies.

Getting Started with TTS

For businesses exploring TTS:

  1. Identify high-volume voice communication needs where TTS could replace or supplement human voice talent
  2. Evaluate cloud TTS services from Google, Amazon, Microsoft, or specialised providers like ElevenLabs and Murf
  3. Test voice quality in your target languages with native speakers who can assess naturalness and appropriateness
  4. Start with internal use cases like training materials or employee notifications before deploying customer-facing TTS
  5. Consider brand voice strategy: Decide whether to use a stock voice or invest in a custom voice that becomes part of your brand identity
Why It Matters for Business

Text-to-Speech technology transforms how businesses communicate at scale. Every automated phone call, voice notification, and audio content piece that currently requires human voice recording can potentially be handled by TTS, dramatically reducing production time and cost while enabling personalisation and multilingual delivery that would be impractical with human speakers alone.

For CEOs, TTS enables a consistent brand voice across every customer touchpoint, in every language, at any time of day, without the cost and logistics of managing voice talent. For CTOs, TTS is a critical component of conversational AI architecture, serving as the output layer that makes chatbots, virtual assistants, and automated systems feel natural and approachable.

In Southeast Asia, TTS is particularly valuable for businesses operating across multiple language markets. Rather than recording customer-facing audio in eight or more languages, TTS allows a single text source to be rendered in any supported language instantly. As neural TTS quality improves for ASEAN languages, this capability becomes increasingly compelling. Companies that build TTS into their communication infrastructure now will be well positioned to scale their voice presence across the region efficiently and consistently.

Key Considerations
  • Evaluate TTS voice quality with native speakers of your target languages. Automated quality scores do not capture cultural appropriateness, accent preferences, or subtle pronunciation issues that matter to real listeners.
  • Consider latency requirements carefully. Real-time TTS for phone systems and voice assistants requires low-latency processing, which may cost more than batch synthesis for pre-generated content.
  • Plan your voice strategy as a brand asset. A distinctive, consistent TTS voice across customer touchpoints strengthens brand recognition, while using different generic voices creates a fragmented experience.
  • Monitor usage costs, which are typically charged per character or per million characters synthesised. High-volume applications like IVR systems can generate significant monthly costs.
  • Ensure your TTS provider handles your domain-specific vocabulary correctly. Technical terms, product names, and acronyms often require custom pronunciation dictionaries.
  • Test the emotional range of TTS voices for customer-facing applications. A voice that sounds natural reading a product description may sound inappropriate delivering a service disruption notification.
  • Consider data residency requirements. If your text content contains sensitive information, verify where TTS processing occurs and whether it complies with local data protection regulations.

Frequently Asked Questions

How natural does modern TTS sound compared to human speech?

Leading neural TTS systems achieve near-human naturalness for well-supported languages like English, Mandarin, and Japanese. In blind listening tests, listeners frequently cannot distinguish top-tier TTS from recorded human speech. For Southeast Asian languages, quality varies. Thai and Vietnamese TTS has improved significantly but may still sound slightly synthetic in complex sentences. Bahasa Indonesia and Malay TTS quality is approaching English-level naturalness from major providers. The key factor is the amount of high-quality training data available for each language.

What does TTS cost for business applications?

Cloud TTS pricing typically ranges from USD 4 to 16 per million characters, depending on voice quality tier. Standard voices are cheaper while neural or custom voices cost more. For context, one million characters is approximately 150,000 to 200,000 words, equivalent to roughly 15-20 hours of spoken audio. A business generating 100 hours of TTS audio per month in a single language might spend USD 30 to 120 monthly. Custom voice creation adds a one-time cost of USD 5,000 to 50,000 depending on the provider and quality level.

More Questions

Yes, major cloud TTS providers like Google Cloud Text-to-Speech and Amazon Polly support multiple ASEAN languages including Thai, Vietnamese, Bahasa Indonesia, and Tagalog. However, the number of available voice options and quality levels differs across languages. English might offer 30+ voice choices while a language like Vietnamese may have only 2-4. Businesses should test each language individually and consider using specialised regional providers for languages where the major platforms underperform.

Need help implementing Text-to-Speech (TTS)?

Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how text-to-speech (tts) fits into your AI roadmap.

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