FastSpeech 2: Improving Text-to-Speech Technology
Text-to-speech (TTS) technology has greatly improved in recent years, but there is still a major challenge it faces called the one-to-many mapping problem. This refers to the issue where multiple speech variations correspond to the same input text, resulting in an inaccurate or robotic-sounding output. To address this problem, researchers have developed a new TTS model called FastSpeech 2, which aims to improve upon the original FastSpeech by directly training the model with ground-truth targets and introducing more variation information of speech as conditional inputs.
How Does FastSpeech 2 Work?
FastSpeech 2 uses a three-part process to convert input text into natural-sounding speech. The first step is the encoder, which converts the phoneme embedding sequence into the hidden sequence. The variance adaptor then adds different variance information like duration, pitch, and energy into the hidden sequence. Lastly, the mel-spectrogram decoder converts the adapted hidden sequence into the mel-spectrogram sequence in parallel.
The biggest difference between FastSpeech 2 and the original model is how it handles the one-to-many mapping problem. FastSpeech 2 extracts duration, pitch, and energy from speech waveforms and directly takes them as conditional inputs during training. It then uses predicted values during inference to create natural-sounding speech. Additionally, FastSpeech 2 uses a feed-forward Transformer block architecture, consisting of self-attention and 1D convolution, for both the encoder and mel-spectrogram decoder structures, which helps improve accuracy and efficiency.
Benefits of FastSpeech 2
The improvements made in FastSpeech 2 offer numerous benefits to both end-users and developers. The model can generate high-quality, natural-sounding speech from text with more accuracy and less roboticness than previous models.
FastSpeech 2 can also be used in various applications such as creating virtual assistants or audiobooks. Because the model generates speech in real-time (meaning it does not require pre-recorded audio), it can be used for any situation requiring instant speech synthesis.
FastSpeech 2 shows great promise in the field of text-to-speech technology. With its more accurate and efficient training process and focus on addressing the one-to-many mapping problem, it has the potential to become a go-to model for synthesizing natural-sounding speech from text.
