It can be brittle, and require expert rule-writers so are hard to maintain.

Cons of Rule-based Normalization

Rule-based normalization has the advantage of not requiring training data and can be designed to achieve high precision.

Advantages of Rule-based Normalization

Rule-based normalization is done in two stages: tokenization and verbalization. 

University of Michigan - Ann Arbor

Text to speech, or speech synthesis, is the inverse problem of automatic speech recognition. It is a sequence-to-sequence learning task where the input is a sequence of text and the output is a generated audio file, meaning the resulting output sequence is significantly longer than the input sequence.

Text to Speech

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

The first component is an encoder-decoder model for spectrogram prediction: it maps from strings of letters to mel spectrographs: sequences of mel spectral values over time. The second component maps from mel spectrograms to waveforms.

Components of TTS

Numbers, monetary amounts, dates, and other concepts that are verbalized differently than they are spelled.

Non-standard Words

Often the verbalization of a non-standard word depends on its meaning. The meaning is called Semiotic Class.

Semiotic Class

Rule-based Normalization

Encoder-decoder-based Models have been shown to work better than rules for such transduction tasks, but do require expert-labeled training sets in which non-standard words have been replaced with the appropriate verbalization; such training sets for some languages are available

Encoder-decoder-based Normalization

The exact same architecture we described for ASR—the encoder-decoder with attention – can be used for the first component of TTS. 

The Tacotron2 architecture is an encoder-decoder that maps from graphmes to spectrograms, followed by a vocoder that maps to wavefiles. 

Spectrogram Prediction

Speech synthesis systems are evaluated by human listeners. We evaluate the quality of synthesized utterances by playing a sentence to listeners and ask them to give a mean opinion score (MOS), a rating of how good the synthesized utterances are, usually on a scale from 1–5. 

Paired t-tests to test for significant differences can be used to compare systems on the same sentence. AB tests can be used to compare exactly two systems.

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