How To Convert Text To Speech
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What neural TTS does differently
Text-to-speech went from robotic-sounding novelty to genuinely human-sounding tool around 2020, when neural TTS models (WaveNet, Tacotron, then Glow-TTS and VALL-E) replaced the older concatenative and formant-synthesis approaches. The difference is striking — modern TTS is used for audiobook narration, podcast ads, IVR systems, and accessibility tools without listeners realizing it’s synthetic. Using TTS well, though, still takes more than copy-pasting text. This guide covers SSML markup for precise control, voice selection criteria, prosody (rate, pitch, volume), the split between the browser’s free Web Speech API and cloud TTS services, and the accessibility considerations that separate “technically reads the text” from “actually useful for screen-reader users.”
SSML: the markup language for TTS
Old TTS pipelines concatenated recorded phonemes (tiny speech fragments) and smoothed the seams. Output sounded segmented and robotic. Neural TTS generates the waveform directly from text using deep learning — either in a two-stage pipeline (text to mel-spectrogram, then neural vocoder to waveform) or end-to-end (text straight to waveform). The result has natural prosody, breathing, and intonation.
Key SSML tags
Current state-of-the-art systems can clone a voice from 3–5 seconds of reference audio, match emotional tone, and even preserve a speaker’s accent across languages. The tradeoff is compute — neural TTS needs a GPU for real-time generation, unlike the old concatenative systems that ran on phones in 2005.
Voice selection
Speech Synthesis Markup Language (SSML) is a W3C standard that lets you control how text is rendered. It looks like HTML with TTS-specific tags.
Prosody: rate, pitch, volume
Not all TTS engines support all SSML tags. AWS Polly and Google Cloud TTS support broad SSML; OpenAI’s TTS API currently supports only plain text. Check your engine’s docs before authoring SSML.
Web Speech API (browser-native)
The voice sets the personality of the output. Most cloud TTS services offer dozens of voices per language, with names (Amazon Polly has Matthew, Joanna, Ivy; Google has wavenet voices coded Male A/B/C; Azure has over 400 neural voices across 100+ languages).
Cloud TTS comparison
Voice choice criteria: match the content’s formality (a news-style voice vs conversational), match the demographic you’re targeting (age, accent, gender), and test with your actual script — some voices handle long sentences better than others.
Pronunciation control
The Web Speech API has no SSML support. You can control rate, pitch, volume per utterance, but not mid-sentence emphasis or pauses. For richer control, use cloud TTS.
Audio output format
For long-form content (audiobooks, podcast production) the cost matters; a 50,000-character chapter is ~$0.20–$0.80. For real-time applications (phone systems, games), latency matters more. ElevenLabs and Azure are the common choices for expressive narration; AWS and Google for high-volume IVR.
Accessibility considerations
TTS engines mispronounce unusual words, brand names, and technical terms. Fixes:
Common mistakes
For a final podcast or video deliverable, request WAV or 320kbps MP3, apply any post-processing (compression, EQ, loudness normalization to -16 LUFS), then export to final format. Don’t use the raw TTS MP3 as-is — post processing makes it sound more professional.
Run the numbers
Screen-reader users consume TTS output hours per day. A few rules for TTS-accessible content: