Text-to-Speech Model Spectrogram Conditioning
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Solution Overview
Problem
Existing text-to-speech (TTS) systems face challenges in efficiently generating natural-sounding speech, particularly in producing diverse audio qualities such as whispered, stern, or excited speech, due to the need for extensive recording and the limitations of unit selection and parametric synthesis techniques.
Innovation Solution
A model is trained to directly generate audio output waveforms sample-by-sample, using a sequence-to-sequence model with encoders for different acoustic features and a decoder to produce spectrogram data, which is then used by a speech model to synthesize speech that resembles the style and tone of a particular speaker.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a model is trained to directly generate audio output waveforms sample-by-sample, then audio quality is improved, but computational cost increases and real-time response is compromised
Solution Approach 1:
The audio generation process is divided into multiple stages: first generating a spectrogram representation, then converting it to audio waveform. This segmentation allows the system to achieve high audio quality through detailed spectrogram processing while managing computational load by breaking down the complex direct waveform generation into more efficient intermediate steps.
Solution Approach 2:
A spectrogram is introduced as an intermediary representation between text input and audio output. The spectrogram serves as a computationally efficient intermediate form that captures audio characteristics without requiring direct sample-by-sample generation, thus improving real-time response while maintaining audio quality through the subsequent waveform synthesis stage.
2Adaptability or versatility
If extensive recording is performed to produce diverse audio qualities, then speech diversity is improved, but time and resource consumption increase
Solution Approach 1:
Instead of recording diverse speech samples for each audio quality, the system changes parameters such as pitch, tone, and spectral characteristics through processing the spectrogram. This allows generation of whispered, stern, excited, and other diverse speech qualities by modifying acoustic parameters rather than requiring separate recordings for each style.
Solution Approach 2:
The system creates synthetic copies of speech with different qualities by processing a base spectrogram through various transformations. Rather than recording original diverse speech samples, the system generates copied and modified versions that exhibit diverse audio qualities, significantly reducing recording time and resource requirements.
Data Source
AI summary
During text-to-speech processing, a speech model creates output audio data, including speech, that corresponds to input text data that includes a representation of the speech. A spectrogram estimator estimates a frequency spectrogram of the speech; the corresponding frequency-spectrogram data is used to condition the speech model. A plurality of acoustic features corresponding to different segments of the input text data, such as phonemes, syllable-level features, and/or word-level features, may be separately encoded into context vectors; the spectrogram estimator uses these separate context vectors to create the frequency spectrogram.