What is Speech Synthesis Markup Language (SSML)?
Speech Synthesis Markup Language (SSML) is an XML-based markup language that provides detailed control over how text-to-speech systems render spoken output. It allows developers to specify pronunciation, prosody, pauses, emphasis, speaking rate, and other speech characteristics that plain text alone cannot convey.
What is Speech Synthesis Markup Language?
Speech Synthesis Markup Language (SSML) is a standardised XML-based language defined by the World Wide Web Consortium (W3C) that gives developers precise control over how text-to-speech (TTS) systems produce spoken audio. When you send plain text to a TTS system, the system must make its own decisions about pronunciation, pacing, emphasis, and intonation. SSML allows you to override these default decisions and specify exactly how the text should be spoken.
Think of SSML as stage directions for a speech synthesiser. Just as a playwright provides actors with directions about volume, pacing, and emotion alongside the dialogue, SSML provides speech synthesis engines with instructions about how to deliver the text.
Why SSML Exists
Plain text contains limited information about how it should be spoken. Consider these challenges that SSML addresses:
- Pronunciation ambiguity: The word "read" can be pronounced with a long "ee" sound (present tense) or a short "e" sound (past tense). SSML specifies which pronunciation to use.
- Numbers and abbreviations: Should "100" be spoken as "one hundred" or "one-zero-zero"? Is "Dr." "doctor" or "drive"? Is "St." "saint" or "street"? SSML resolves these ambiguities.
- Emphasis and pacing: Plain text provides no way to indicate that a particular word should be emphasised or that a longer pause should occur between sentences. SSML adds this expressive control.
- Multilingual content: When text contains words or phrases from different languages, SSML specifies which language model should be used for each segment.
Key SSML Elements
Break
Inserts a pause at a specific point with controllable duration. Used for dramatic effect, natural phrasing, or allowing the listener time to absorb information.
Emphasis
Marks words or phrases for emphasis, with levels from reduced to strong. Affects pitch, rate, and volume to draw attention to specific content.
Prosody
Controls pitch, rate, and volume of speech across a passage. Allows fine-tuning of the overall delivery style from conversational to formal, from fast to slow, from quiet to loud.
Say-As
Specifies how particular content types should be spoken. Dates, times, telephone numbers, currencies, and addresses all have specific spoken forms that differ from their written representation.
Phoneme
Provides explicit phonetic pronunciation using the International Phonetic Alphabet (IPA) or other phonetic systems. Essential for proper names, technical terms, and words with non-standard pronunciations.
Language
Specifies the language for a section of text, enabling correct pronunciation for multilingual content. A Thai name in an English sentence, for example, should be pronounced using Thai phonology.
Voice
Selects a specific voice or voice characteristics (gender, age, style) for a section of content. Enables multiple voices in a single output, such as a dialogue between characters.
Business Applications
Voice Assistants and IVR Systems
Interactive voice response (IVR) systems for customer service use SSML to ensure proper pronunciation of company names, product names, account numbers, and addresses. Without SSML, TTS systems frequently mispronounce business-specific terminology.
Content Narration
Publishers and media companies converting written content to audio use SSML to control pacing, emphasis, and pronunciation, producing more engaging and accurate narration than plain text synthesis.
Accessibility
Screen readers and accessibility tools use SSML to provide more natural and informative speech output, including appropriate pauses at structural boundaries and correct pronunciation of technical content.
E-Learning
Educational content delivered through speech uses SSML to pace information delivery appropriately, emphasise key terms, spell out acronyms on first use, and handle multilingual vocabulary correctly.
Notifications and Alerts
Automated notification systems use SSML to convey urgency through prosody control, ensuring that critical alerts sound different from routine notifications.
Localised Content
Businesses operating across multiple markets use SSML to handle the pronunciation challenges of localised content, including addresses, names, currencies, and regulatory terminology specific to each market.
SSML in Southeast Asian Applications
SSML is particularly valuable in Southeast Asia due to the region's linguistic complexity:
- Multilingual content: Business communications across ASEAN frequently mix languages. SSML allows a TTS system to switch between English, Thai, Malay, or other languages within a single document, pronouncing each section correctly.
- Proper name pronunciation: Southeast Asian names often challenge TTS systems designed for Western languages. SSML phonetic notation ensures correct pronunciation of Thai, Vietnamese, Indonesian, and other regional names.
- Tonal language handling: For tonal languages like Thai and Vietnamese, SSML provides mechanisms to specify tonal pronunciation when the TTS system might otherwise select the wrong tone.
- Number and currency formatting: Different ASEAN countries format numbers, currencies, and dates differently. SSML say-as tags ensure these are spoken according to local conventions.
- IVR systems: Contact centres across the region use SSML to build IVR systems that correctly pronounce local place names, product names, and customer information across multiple languages.
Platform Support
Major cloud TTS platforms support SSML with varying levels of completeness:
- Google Cloud Text-to-Speech: Comprehensive SSML support with extensions for audio effects and voice selection
- Amazon Polly: Full SSML support with additional tags for whispered speech and breathing effects
- Microsoft Azure Speech: Standard SSML support with extensions for neural voice styles and emotional expression
- IBM Watson TTS: Standard SSML support with good multilingual capabilities
Each platform supports the core SSML standard but adds proprietary extensions. Applications targeting multiple platforms should use standard SSML tags where possible and isolate platform-specific features.
Challenges and Limitations
Complexity: SSML markup can become verbose and difficult to maintain for large documents. Automated SSML generation tools can help manage complexity.
Inconsistent implementation: Despite the W3C standard, different TTS platforms implement SSML with varying levels of completeness and interpret some tags differently.
Limited expressiveness: While SSML provides significant control, it cannot fully capture the nuance of human speech direction. The gap between what SSML can specify and what a human voice director can communicate remains significant.
Getting Started
- Learn the core tags: Start with break, emphasis, say-as, and phoneme, which address the most common TTS quality issues
- Test on your target platform: Verify that your SSML produces the desired results on the specific TTS engine you are using
- Build incrementally: Add SSML markup to address specific pronunciation or delivery issues rather than trying to mark up everything at once
- Create a pronunciation dictionary: Maintain a library of SSML phoneme specifications for company-specific terms, names, and abbreviations
- Automate where possible: Build tools that automatically insert common SSML patterns, such as number formatting and name pronunciation, into your content
SSML is the quality control layer for speech synthesis applications. For business leaders deploying voice-enabled services, SSML is the difference between TTS output that mispronounces your company name, rushes through important information, and stumbles over technical terms, and TTS output that sounds polished, professional, and trustworthy.
The business impact is most visible in customer-facing voice applications. An IVR system that correctly pronounces customer names, product names, and addresses builds trust and reduces frustration. A voice assistant that delivers information with appropriate pacing and emphasis is perceived as more competent and helpful. Content narration that handles technical vocabulary and multilingual terms correctly maintains the credibility of the original content.
For Southeast Asian businesses operating across multiple languages and markets, SSML capability is essential for professional-quality voice applications. The region's linguistic diversity means that TTS systems will inevitably encounter names, terms, and language mixtures that default pronunciation cannot handle correctly. Companies that invest in SSML expertise can build voice applications that work correctly across ASEAN markets, while competitors relying on default TTS pronunciation will frustrate users with mispronunciations and awkward delivery.
- Maintain a centralised pronunciation dictionary in SSML format for your company-specific terms, product names, and frequently used proper names. This ensures consistency across all voice applications.
- Test SSML output with native speakers of each target language. What sounds acceptable to non-native speakers may contain obvious pronunciation errors that native speakers find jarring.
- Keep SSML markup as simple as possible. Over-marking text with excessive prosody control often sounds less natural than well-chosen minimal markup.
- Plan for SSML maintenance as your product names, terminology, and content evolve. Pronunciation dictionaries need regular updates.
- Consider SSML generation tools or templates for high-volume content conversion. Manual SSML markup is impractical for large content libraries.
- Test across all platforms you support. SSML that works perfectly on one TTS engine may produce unexpected results on another due to implementation differences.
- Balance control with naturalness. Modern neural TTS systems often produce better results with less SSML intervention than older systems required. Over-constraining a neural voice can sometimes produce less natural output than letting it use its learned prosody.
Frequently Asked Questions
Do we need SSML if we are using a modern neural text-to-speech system?
Modern neural TTS systems are significantly better at handling pronunciation, pacing, and emphasis than older systems, reducing the need for extensive SSML markup. However, SSML remains valuable for several scenarios: correcting mispronunciation of proper names and domain-specific terms, controlling the delivery of numbers, dates, and abbreviations, managing multilingual content where the system needs to switch language models, and adding specific pauses or emphasis for clarity. A practical approach is to start without SSML and add markup selectively to address specific issues rather than marking up all content comprehensively.
How much effort is required to implement SSML in our voice application?
The effort depends on your content complexity and quality requirements. For simple applications with limited vocabulary, adding SSML for a pronunciation dictionary of 50 to 200 terms might take one to two days. For complex applications with extensive domain vocabulary, multilingual content, and high quality expectations, SSML implementation and testing might take two to four weeks. Ongoing maintenance typically requires a few hours per month to add new terms and address pronunciation issues reported by users. The investment is modest compared to the impact on user experience quality, and most organisations find that building SSML expertise within their development team is straightforward.
More Questions
SSML provides tools that address many Southeast Asian language challenges, but effectiveness depends on the underlying TTS engine quality. The phoneme tag can specify exact pronunciation using IPA notation, which is valuable for tonal languages where incorrect tone selection changes word meaning. The language tag enables switching between languages in multilingual content. The say-as tag handles region-specific number, date, and currency formats. However, SSML cannot compensate for a TTS engine that fundamentally lacks good support for a particular language. For best results, select a TTS platform with strong native support for your target Southeast Asian languages and use SSML to fine-tune specific pronunciation and delivery issues rather than trying to fix fundamental language model limitations.
Need help implementing Speech Synthesis Markup Language (SSML)?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how speech synthesis markup language (ssml) fits into your AI roadmap.