To adjust speech speed and pitch in text-to-speech (TTS) systems, developers have several options depending on the TTS engine and use case. These include API parameters, markup languages, model-level controls, and post-processing tools. Each method varies in implementation complexity and granularity of control.
API Parameters and Configuration
Most TTS APIs provide direct parameters to control speed and pitch. For example, Google Cloud Text-to-Speech uses speaking_rate for speed (1.0 = normal, 2.0 = double speed) and pitch (measured in semitones) to raise or lower the voice’s baseline frequency. Similarly, Amazon Polly offers rate (e.g., "fast", "slow") and pitch settings. These parameters adjust the synthesized speech during generation, ensuring minimal quality loss. For neural TTS models like Tacotron or FastSpeech, speed is often controlled by modifying the duration predictor’s output, while pitch can be adjusted via separate pitch prediction modules. This approach integrates changes directly into the synthesis process, avoiding artifacts from post-processing.
SSML and Markup-Based Controls
Speech Synthesis Markup Language (SSML) enables fine-grained adjustments within the input text. Using tags like <prosody>, developers can set rate, pitch, and even contour (dynamic pitch changes) for specific words or phrases. For example, <prosody rate="0.8" pitch="high">Hello</prosody> would slow down the word "Hello" and raise its pitch. This is useful for adding emphasis or natural variation. Tools like Microsoft Azure Cognitive Services and IBM Watson support SSML, allowing real-time adjustments without requiring audio post-processing. However, SSML syntax and supported features vary across TTS providers, requiring platform-specific implementation.
Post-Processing and External Tools
If the TTS engine lacks built-in controls, developers can modify generated audio using libraries like FFmpeg or SoX. For example, SoX’s tempo effect adjusts speed without altering pitch, while the pitch effect shifts frequency. However, these tools may introduce artifacts, especially with extreme adjustments. Neural vocoders like HiFi-GAN or WaveGlow can also resynthesize audio with modified speed or pitch, but this requires re-running the TTS pipeline. Post-processing is a fallback option but adds complexity and may degrade output quality compared to integrated solutions.
In summary, the choice depends on the TTS system’s capabilities and the application’s needs. API parameters and SSML offer real-time, high-quality adjustments, while post-processing provides flexibility at the cost of additional steps and potential quality trade-offs.