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Text to Speech Deep Learning Architectures

Small Intro. and Background

Recently, I started at Mozilla Research. I am really excited to be a part of a small but a great team working hard to solve important ML problems. And everything is open-sourced. We license things to make open-sourced. Oxymoron by the first sight, isn't it. But I like it !!

Before my presence, our team already released the best known open-sourced STT (Speech to Text) implementation based on Tensorflow. The next step is to improve the current Baidu's Deep Speech architecture and also implement a new TTS (Text to Speech) solution that complements the whole conversational AI agent. So after these two projects, anyone around the world will be able to create his own Alexa without any commercial attachment. Which is the real way to democratize AI, at least I believe it is.

Up until now, I worked on variety of data types and ML problems, except audio. Now it is time learn it. And the first thing to do is a comprehensive literature review (like a boss). Here I like to share the top notch DL architectures dealing with TTS (Text to Speech). I also invite you to our Github repository hosting PyTorch implementation of the first version implementation. (We switched to PyTorch for obvious reasons). It is a work in progress and please feel free to comment and contribute.

Below I like to share my pinpoint summary of the well-known TTS papers which are by no means complete but useful to highlight important aspects of these papers. Let's start.

Glossary

  • Prosody: https://en.wikipedia.org/wiki/Prosody_(linguistics)
  • Phonemes : units of sounds, we pronounce as we speak. Necessary since very similar words in letter might be pronounced very differently (e.g. "Rough" "Though")
  • Vocoder: part of the system decoding from features to audio signals. Wave is used in Deep Voice at that stage.
  • Fundamental Frequency - F0: lowest frequency of a periodic waveform describing the pitch of the sound.
  • Autoregressive Model: Specifies a model depending linearly on its own outputs and on a parameter set which can be approximated.
  • Query, Key, Value: Key is used by attention module to compute attention weights. Value is the vector stipulated by the attention weights to compute the module output. Query vector is the hidden state of the decoder.
  • Grapheme: Cool way to say character.
  • Error Modes: Sub-optimal status for the attention block where it is not able to escape.
  • Monotonic Attention: Use only a limited scope of nodes close in time to the output step. It improves performance for TTS since there is a certain relation btw the output at time t and the input at time t. However, it is not that reasonable for translation problem since words orders might no be the same. https://arxiv.org/pdf/1704.00784.pdf
  • MOS: Mean Opinion Score. Crows-source the evaluation process with native speakers. It is not easy to measure, especially for a layman.
  • Context vector: Output of an attention module which summarizes multiple time-step output of the encoder.
  • Hann Window Function: https://en.wikipedia.org/wiki/Window_function#Hann_window
  • Teacher Forcing: Providing model's expected output at time t as a input at time t+1. It is controlled ground-truth feedback as a teacher does to a student.
  • Casual convolution: Convolution which does not foresee the future units given the reference time step T which we like to predict next. In practice, it is implemented by setting right padding orientation to to normal convolution layers.

Deep Voice (25 Feb 2017)

  • Text to phonemes. Deterministically computed with a dictionary. Or Seq2Seq model to deal with the unseen words.
  • The same phoneme might hold different durations in different words. We need to predict the duration. It is sequence depended.
  • Fundamental frequency for the pitch of the each phoneme. It is sequence depended.
  • Frequency + Phonemes + Duration = Voice synthesis. It is done via Google's WaveNet.
  • Models
    • Segmentation Model
      • Segment audio signal to phonemes.
      • CTC loss
      • Predict phoneme pairs due to probability mass
      • Inputs:
        • Audio clip of “It was early spring”
        • Phonemes (label)
          • [IH1, T, ., W, AA1, Z, ., ER1, L, IY0, ., S, P, R, IH1, NG, .]
      • Outputs:
        • Pairs of Phonemes with their start time
          • [(IH1, T, 0:00), (T, ., 0:01), (., W, 0:02), (W, AA1, 0:025), (NG, ., 0:035)]
    • Fundamental Freq & Duration Models
      • Segmentation model predictions are the labels for these models.
      • Inputs:
        • Phonemes
      • Outputs:
        • Duration, Probability, F0 for each phoneme; [H, 0.1, 25hz], ...
    • Audio Synthesizer Model
      • Simplified WaveNet
      • Inputs:
        • Duration and F0 for phonemes + audio signals (labels)
      • Outputs:
        • Synthesis audio signal

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