Google Text To Speech Voices

Maximizing Accessibility and User Engagement with Google Text-to-Speech Voice Customization

Routine automation fails when it doesn’t account for user diversity. Off-the-shelf voices from Google Text-to-Speech (TTS) will get you fast results, but rarely deliver the clarity or emotional tone required for specialized domains—especially in accessibility, tutoring, and branded digital interactions. Time to move beyond defaults.

Voice Customization: Beyond Defaults

Google Cloud Text-to-Speech (tested as recently as v1, 2024-05) supports more than 380 voices, dozens of languages, and the WaveNet neural model. But all the capacity in the world is moot unless the output serves your user group. Too often, developers deploy whatever’s quickest, missing opportunities to increase comprehension and engagement.

Customization parameters that matter:

Known issue: Wavenet voice selection can occasionally mismatch gender/region; always verify voice output in staging.

Getting Started (Assuming Python 3.10+, google-cloud-texttospeech==2.16.0)

1. Create/Select a Google Cloud Project

2. Enable the TTS API
   Dashboard: APIs & Services → Library.

3. Provision Service Account
   Grant role: “Text to Speech Admin”.
   Download JSON credentials and export as GOOGLE_APPLICATION_CREDENTIALS.

export GOOGLE_APPLICATION_CREDENTIALS=~/secrets/google/tts-creds.json

Enumerate Voices

Critical for non-English use cases or where local accent matters. Retrieve the list via CLI:

gcloud ml speech voices list

or, with curl (access token required):

curl -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
  "https://texttospeech.googleapis.com/v1/voices"

Look for codes like en-GB-Wavenet-B or es-ES-Standard-A. If the distribution changes—some voices are deprecated—check official docs.

Fine-Tuning with SSML and Parameters

Default settings produce generic results. In accessibility contexts (e.g., screen readers), users respond better to adjusted pitch for teenagers, or slower rate for cognitive disabilities. Branded bots may demand consistent emphasis or even regional humor in speech.

Sample payload with custom settings and SSML (JSON):

{
  "input": {
    "ssml": "<speak>System status: <break time=\"300ms\"/> All services operational.</speak>"
  },
  "voice": {
    "languageCode": "en-US",
    "name": "en-US-Wavenet-F",
    "ssmlGender": "FEMALE"
  },
  "audioConfig": {
    "audioEncoding": "MP3",
    "pitch": "-1.5",
    "speakingRate": "0.95"
  }
}

Note: For notification bots, a lower pitch and slightly reduced rate decrease error rates with elderly users. But, using extremes (e.g., pitch +15) will quickly tire listeners.

Example Implementation (Python)

Typical usage: convert announcement text to MP3 for mobile playback or VOIP insertion.

from google.cloud import texttospeech

client = texttospeech.TextToSpeechClient()

input_text = texttospeech.SynthesisInput(ssml="""
<speak>
Critical alert.
<break time="600ms"/>
Database latency exceeds threshold.
<prosody pitch="-2st" rate="slow">Investigate immediately.</prosody>
</speak>
""")

voice = texttospeech.VoiceSelectionParams(
    language_code="en-US",
    name="en-US-Wavenet-D",
    ssml_gender=texttospeech.SsmlVoiceGender.MALE,
)

audio_config = texttospeech.AudioConfig(
    audio_encoding=texttospeech.AudioEncoding.MP3,
    pitch=-2.0,
    speaking_rate=0.98,
)

response = client.synthesize_speech(
    input=input_text, voice=voice, audio_config=audio_config
)

with open("alert.mp3", "wb") as out:
    out.write(response.audio_content)

Gotcha: If you overuse <break> tags, GCP returns INVALID_ARGUMENT: Found an unexpected break tag.. Test for SSML compliance.

Practical Guidance

• User feedback first: Select and iterate voice parameters based on actual user testing—local dialect rails against “standard US” defaults.
• SSML isn’t a magic fix: Over-formatting quickly leads to unnatural phrasing.
• Performance: WaveNet increases latency (~200–300ms per request). For real-time systems, buffer or cache output.
• Cost control: API pricing varies by voice type (WaveNet is ~4x standard). Batch synthesize where feasible.
• Accessibility note: For screen readers, avoid using voices set to max speed or altered pitch more than ±5 from default.

Conclusion

Straightforward parameter tweaks can double comprehension rates or reinforce brand character. But don’t trust defaults—or your own ears—alone; always A/B test with real users. In some cases, Google’s TTS isn’t enough (see Amazon Polly for Mandarin nuance, for example), so keep alternatives in mind.

Non-obvious Tip

For internationalization, dynamically switch both the languageCode and the speaking rate based on user agent locale. E.g., for tr-TR (Turkish), reduce rate to 0.88—comprehension increases significantly in field trials.

Custom voice tuning isn’t optional for serious products. Audit your TTS pipeline; mismatches here are harder to debug than regular code errors.