Speech synthesis: A review of the best text to speech architectures with Deep Learning

Speech synthesis is the duty of producing speech from another modality like textual content, lip actions, and so on. In most purposes, textual content is chosen because the preliminary kind due to the fast advance of pure language methods. A Textual content To Speech (TTS) system goals to transform pure language into speech.

Over time there have been many alternative approaches, with probably the most outstanding being concatenation synthesis and parametric synthesis.

Concatenation synthesis

Concatenation synthesis, because the identify suggests, is predicated on the concatenation of pre-recorded speech segments. The segments could be full sentences, phrases, syllables, diphones, and even particular person telephones. They’re normally saved within the type of waveforms or spectrograms.

We purchase the segments with the assistance of a speech recognition system and we then label them primarily based on their acoustic properties (e.g. their elementary frequency). At run time, the specified sequence is created by figuring out the most effective chain of candidate items from the database (unit choice).

Statistical Parametric Synthesis

Parametric synthesis makes use of recorded human voices as nicely. The distinction is that we use a perform and a set of parameters to change the voice. Let’s break that down:

Statistical parametric speech synthesis

In statistical parametric synthesis, we usually have two elements. The coaching and the synthesis. Throughout coaching, we extract a set of parameters that characterize the audio pattern such because the frequency spectrum (vocal tract), elementary frequency (voice supply), and length (prosody) of speech. We then attempt to estimate these parameters utilizing a statistical mannequin. The one which has been confirmed to offer the most effective outcomes traditionally is the Hidden Markov Mannequin (HMM).

Throughout synthesis, HMMs generate a set of parameters from our goal textual content sequence. The parameters are used to synthesize the ultimate speech waveforms.

Benefits of statistical parametric synthesis:

Nonetheless, generally, the standard of the synthesized speech shouldn’t be ideally suited. That is the place Deep Studying primarily based strategies come into play.

However earlier than that, I want to open a small parenthesis and talk about how we consider speech synthesis fashions.

Speech synthesis analysis

Imply Opinion Rating (MOS) is probably the most steadily used methodology to judge the standard of the generated speech. MOS has a spread from 0 to five the place actual human speech is between 4.5 to 4.8

MOS comes from the telecommunications subject and is outlined because the arithmetic imply over single scores carried out by human topics for a given stimulus in a subjective high quality analysis take a look at. This traditionally implies that a bunch of individuals sits in a quiet room, listens to the generated pattern, and offers it a rating. MOS is nothing greater than the typical of all “folks’s opinion”

Right this moment’s benchmarks are carried out over completely different speech synthesis datasets in English, Chinese language, and different fashionable languages. You’ll find such benchmarks in paperswithcode.com

Speech synthesis with Deep Studying

Earlier than we begin analyzing the varied architectures, let’s discover how we are able to mathematically formulate TTS.

Given an enter textual content sequence $mathbf{Y}$ , the goal speech $mathbf{X}$ could be derived by:

the place $theta$ is the mannequin’s parameters.

In most fashions, we first move the enter textual content to an acoustic function generator, which produces a set of acoustic options comparable to the elemental frequency or spectrogram.

To generate the ultimate speech section, a Neural vocoder is often used.

A conventional vocoder is a class of voice codec which encrypts and compresses the audio sign and vice versa. This was historically completed by means of digital sign processing strategies. A neural vocoder achieves the encoding/decoding utilizing a neural community.

WaveNet

WaveNet was the primary mannequin that efficiently modeled the uncooked waveform of the audio sign as an alternative of the acoustic options. It is ready to generate new speech-like waveforms at 16,000 samples per second.

WaveNet in its core is an autoregressive mannequin the place every pattern depends upon the earlier ones. Mathematically, this may be expressed as :

In essence, we factorize the becoming a member of likelihood of the waveform as a product of conditional possibilities of the earlier time steps.

To construct such autoregressive fashions, the authored used a totally convolutional neural community with dilated convolutions. WaveNet was impressed by PixelCNN and PixelRNN, that are in a position to generate very complicated pure photographs.

Supply:WaveNet: A generative mannequin for uncooked audio

As we are able to see within the picture above, every convolutional layer has a dilation issue. They used actual waveforms recorded from human audio system throughout coaching. After coaching, the ultimate waveform is produced by sampling from the community. How the sampling is carried out?

The autoregressive mannequin computes the likelihood distribution $p_{theta }(mathbf {x} )$

• We pattern a price from the distribution

• We feed the worth again to the enter, and the mannequin generates the brand new prediction

• We proceed this process one step at a time to generate all the speech waveform.

That is the principle shortcoming of WaveNet. As a result of we have to carry out this for each easy pattern, inferences can grow to be very gradual and computationally costly

The primary model of WaveNet managed to has a MOS of 4.21 within the English language the place for earlier state of artwork fashions, MOS was between 3.67 and three.86.

Quick WaveNet

Quick WaveNet managed to scale back the complexity of the unique WaveNet from $O( 2^L)$ to $O(L)$ the place $L$ is the variety of layers within the community. This was achieved by introducing a caching system that saved earlier calculations. That approach no redundant convolutions have been ever be calculated.

The caching scheme of Quick WaveNet. Supply: Quick WaveNet Technology Algorithm

Deep Voice

Deep Voice by Baidu laid the inspiration for the later developments on end-to-end speech synthesis. It consists of 4 completely different neural networks that collectively kind an end-to-pipeline.

1. A segmentation mannequin that locates boundaries between phonemes. It’s a hybrid CNN and RNN community that’s skilled to foretell the alignment between vocal sounds and the goal phonemes utilizing the CTC loss.

2. A mannequin that converts graphemes to phonemes. A multi-layer encoder-decoder mannequin with GRU cells was chosen for this job.

3. A mannequin to foretell phonemes length and the elemental frequencies. Two absolutely linked layers adopted by two unidirectional GRU layers and one other absolutely linked layer, have been skilled to be taught each duties concurrently

4. A mannequin to synthesize the ultimate audio. Right here the authors carried out a modified WaveNet. The WaveNet consists of a conditioning community that upsamples linguistic options to the specified frequency, and an autoregressive community, which generates a likelihood distribution P over discretized audio samples

System diagram depicting (a) coaching process and (b) inference process of DeepVoice. Supply: Deep Voice: Actual-time Neural Textual content-to-Speech

Additionally they managed to attain real-time inference by developing extremely optimized CPU and GPU kernels to hurry up the inference. It obtained a MOS of two.67 in US English.

Tacotron

Tacotron was launched by Google in 2017 as an end-to-end system. It’s mainly a sequence to sequence mannequin that follows the acquainted encoder-decoder structure. An consideration mechanism was additionally utilized.

Supply: Tacotron: In direction of Finish-to-Finish Speech Synthesis

Let’s break down the above diagram.

The mannequin takes as enter characters and outputs the uncooked spectrogram of the ultimate speech, which is then transformed to waveform.

The CBHG module

You may marvel what is that this CBHG. CBHG stands for: 1-D convolution financial institution + freeway community + bidirectional GRU. The CBHG module is used to extract representations from sequences, and it was initially developed for neural machine translation. The under diagram will provide you with a greater understanding:

The CBHG module. Supply: Tacotron: In direction of Finish-to-Finish Speech Synthesis

Again to Tacotron. The encoder’s purpose is to extract strong sequential representations of textual content. It receives a personality sequence represented as one-hot encoding and thru a stack of PreNets and CHBG modules, it outputs the ultimate illustration. PreNet is used to explain the non-linear transformations utilized to every embedding.

Content material-based consideration is used to move the illustration to the decoder, the place a recurrent layer produces the eye question at every time step. The question is concatenated with the context vector and handed to a stack of GRU cells with residual connections. The output of the decoder is transformed to the top waveform with a separate post-processing community, containing a CBHG module.

Tacotron achieved a MOS of three.82 on an US English analysis set.

Deep Voice 2

Deep Voice 2 got here as an enchancment of the unique Deep Voice structure. Whereas the principle pipeline was fairly related, every mannequin was created from scratch to reinforce its efficiency. One other huge enhancement was the addition of multi-speaker assist.

Key factors of the structure:

• Separation of the phoneme length and elementary frequency fashions

• Speaker embeddings have been launched on every mannequin to attain multiple-speaker capabilities. The speaker embeddings maintain the distinctive info per speaker and are used to provide recurrent neural community (RNN) preliminary states, nonlinearity biases, and multiplicative gating elements, used all through the networks.

• Batch normalization and residual connections have been utilized to the essential fashions

Segmentation, length and frequency fashions of DeepVoice 2. Supply: Deep Voice 2: Multi-Speaker Neural Textual content-to-Speech

A stunning reality is that the authors confirmed, in the identical paper, that we are able to additionally improve Tacotron to assist multi-speakers utilizing related strategies. Furthermore, they substitute Tacotron’s spectrogram-to-waveform Mannequin with their very own WaveNet-based neural vocoder and the outcomes have been very promising

DeepVoice 2 with an 80-layer WaveNet, because the sound synthesizer mannequin, achieved a MOS of three.53

Deep Voice 3

Deep Voice 3 is a whole redesign of the earlier variations. Right here we’ve a single mannequin as an alternative of 4 completely different ones. Extra particularly, the authors proposed a fully-convolutional character-to-spectrogram structure which is right for parallel computation. Versus RNN-based fashions. They have been additionally experimenting with completely different waveform synthesis strategies with the WaveNet attaining the most effective outcomes as soon as once more.

Deep Voice 3: Scaling Textual content-to-Speech with Convolutional Sequence Studying

As you may see, Deep Voice 3 is an encoder-decoder structure and is ready to produce a wide range of textual options(character, phonemes, and so on.) to a wide range of vocoder parameters.

The encoder is a fully-convolutional neural community that transforms textual options right into a compact illustration. The decoder is one other fully-convolutional community that converts the discovered illustration right into a low-dimensional audio illustration. That is achieved utilizing a multi-hop convolutional consideration mechanism.

The convolution block contains 1-D convolutions adopted by a GRU cell and a residual connection.

The convolution block of Deep Voice 3. Supply: Deep Voice 3: Scaling Textual content-to-Speech with Convolutional Sequence Studying

The eye mechanism makes use of a question vector (the hidden states of the decoder) and the per-timestep key vectors from the encoder to compute consideration weights. It then outputs a context vector because the weighted common of the worth vectors.

Consideration block of Deep Voice 3. Supply: Deep Voice 3: Scaling Textual content-to-Speech with Convolutional Sequence Studying

Deep Voice 3 with WaveNet achieved a MOS of three.78 on the time of publishing

Parallel WaveNet

Parallel WaveNet goals to resolve the complexity and efficiency problems with the unique WaveNet, which depends on sequential technology of the audio, one pattern at a time.

They launched an idea referred to as Likelihood Density Distillation that tries to marry Inverse autoregressive flows with environment friendly WaveNet coaching strategies.

Inverse autoregressive flows (IAFs) characterize a form of twin formulation of deep autoregressive modelling, during which sampling could be carried out in parallel. IAFs are stochastic generative fashions whose latent variables are organized so that each one parts of a excessive dimensional observable pattern could be generated in parallel

Let’s break that down and clarify it in easy phrases:

As a result of every pattern depends upon the earlier ones, we are able to’t easy parallelized this course of and compute them in parallel. As a substitute, we begin out from easy white noise and apply modifications over time till it morphs to the specified output waveform. These modifications are utilized to all the sign in a parallel style. How?

We use a teacher-student relationship. The trainer is the unique Community that holds the bottom fact however it’s fairly gradual. The scholar is the brand new community that tries to imitate the trainer however in a extra environment friendly approach.

In response to the authors: “To emphasize the truth that we’re coping with normalized density fashions, we check with this course of as Likelihood Density Distillation (in distinction to Likelihood Density Estimation). The fundamental thought is for the scholar to try to match the likelihood of its personal samples underneath the distribution discovered by the trainer”

Overview of Likelihood Density Distillation. Supply: Parallel WaveNet: Quick Excessive-Constancy Speech Synthesis

Parallel WaveNet is 1000 occasions sooner than the unique networks and might produce 20 seconds of audio in 1 second.

Additionally, observe that related strategies with IAFs to parallelize wave technology have additionally been utilized by different architectures comparable to ClariNet

Tacotron 2

Tacotron 2 improves and simplifies the unique structure. Whereas there aren’t any main variations, let’s see its key factors:

• The encoder now consists of three convolutional layers and a bidirectional LSTM changing PreNets and CHBG modules

• Location delicate consideration improved the unique additive consideration mechanism

• The decoder is now an autoregressive RNN fashioned by a Pre-Internet, 2 uni-directional LSTMs, and a 5-layer Convolutional Put up-Internet

• A modified WaveNet is used because the Vocoder that follows PixelCNN++ and Parallel WaveNet

• Mel spectrograms are generated and handed to the Vocoder versus Linear-scale spectrograms

• WaveNet changed the Griffin-Lin algorithm utilized in Tacotron 1

Tacotron 2. Supply: Pure TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions

Tacotron 2 obtained a formidable MOS of 4.53.

World Type Tokens (GST)

World Type Tokens is a brand new thought to reinforce Tacotron-based architectures . The authors proposed a financial institution of embeddings that may be skilled collectively with Tacotron in an unsupervised method (additionally referred as GST-Tacotron). The embeddings characterize the acoustic expressiveness of various audio system and are skilled with no express labels. In different phrases, they goal to mannequin completely different talking kinds.

Supply: Type Tokens: Unsupervised Type Modeling, Management and Switch in Finish-to-Finish Speech Synthesis

Throughout coaching, a reference encoder is used to extract a fixed-length vector which encodes the details about the talking type (also referred to as prosody). That is then handed to the “type token layer”, an consideration layer that calculates the contribution of every token to the ensuing type embedding.

Throughout inference, a reference audio sequence can be utilized to provide a mode embedding or we are able to manually management the speech type.

TTS with Transformers

Transformers are dominating the Pure Language subject for some time now, so it was inevitable that they’ll progressively enter the TTS subject. Transformers-based fashions goal to deal with two issues of earlier TTS strategies comparable to Tacotron2:

The primary transformers-based structure launched in 2018 and changed RNNs with multi-head consideration mechanisms that may be skilled in parallel.

Supply: Neural Speech Synthesis with Transformer Community

As you may see above, the proposed structure resembles the Transformer proposed within the well-known “Consideration is all you want” paper. In additional particulars we’ve:

• A Textual content-to-Phoneme Converter: converts textual content to phonemes

• Scaled positional encoding: they use a sinusoidal kind that captures details about the place of phonemes

• An Encoder Pre-Internet: a 3-layer CNN just like Tacotron 2, which learns the phonemes embeddings

• A Decoder Pre-Internet: consumes a mel spectogram and initiatives it into the identical subspace as phoneme embeddings

• The Encoder: The bi-directional RNN is changed with a Transformer Encoder with multi-head consideration

• The Decoder: The two-layer RNN with location-sensitive consideration is changed by a Transformer decoder with multi-head self-attention

• Mel Liner and Cease Linear: Two completely different linear projections are used to foretell the mel spectrogram and the cease token respectively

The Transformer-based system achieved a MOS of 4.39.

FastSpeech

An identical method with Transformers is adopted by FastSpeech. FastSpeech managed to hurry up the aforementioned structure by 38x. Briefly, this was completed by the next 3 issues:

• Parallel mel-specogram technology

• Exhausting alignment between phonemes and their mel-spectograms in distinction to mushy consideration alignments within the earlier mannequin

• A size regulator that may simply alter voice pace by lengthening or shortening the phoneme length to find out the size of the generated mel spectrograms,

Supply: FastSpeech: Quick, Strong and Controllable Textual content to Speech

In the identical path, Quick Speech 2 and FastPitch got here later and improved upon the unique thought.

Circulation-based TTS

Earlier than we look at flow-based TTS, let’s clarify what flow-based fashions are. Opposite with GANs and VAEs which approximate the likelihood density perform of our knowledge $p(x)$, Circulation-based fashions do precisely that with the assistance of normalizing flows.

Normalizing Flows are a technique for developing complicated distributions by remodeling a likelihood density by means of a collection of invertible mappings. By repeatedly making use of a predefined rule for change of variables, the preliminary density ‘flows’ by means of the sequence of invertible mappings. On the finish of this sequence, we receive a sound likelihood distribution and therefore one of these circulation is known as a normalizing circulation. For extra particulars, try the unique paper

Numerous fashions have been proposed primarily based om that concept with the most well-liked being RealNVP, NICE and Glow. You possibly can take a look at this glorious article by Lillian Weng to get a extra full understanding.

In order you will have guessed, Circulation-based TTS fashions reap the benefits of this concept and apply it on speech synthesis.

WaveGlow

WaveGlow by Nvidia is likely one of the hottest flow-based TTS fashions. It basically tries to mix insights from Glow and WaveNet with the intention to obtain quick and environment friendly audio synthesis with out using auto-regression. Word that WaveGlow is used strictly to generated speech from mel spectograms changing WaveNets. They aren’t end-to-end TTS methods.

Waveglow. Supply: WaveGlow: A Circulation-based Generative Community for Speech Synthesis

The mannequin is skilled by minimizing the adverse log-likelihood perform of the info. To realize that, we have to use Invertible Neural Networks as a result of in any other case, the perform is intractable. I gained’t go into many particulars as a result of we would want a separate article to elucidate every little thing however right here are some things to recollect:

• Invertible neural networks are normally constructed utilizing coupling layers. On this case, the authors used affine coupling layers

• Additionally they used 1×1 invertible convolutions following the Glow paradigm

As soon as the mannequin is skilled, the inference is solely a matter of randomly sampling values and run them by means of the community

Related fashions embrace Glow-TTS and Circulation-TTS. Flowtron, however, makes use of an Autoregressive Circulation-based Generative Community to generate speech. So we are able to see that there are analysis works in each areas of flow-based fashions.

GAN-based TTS and EATS

Lastly, I’d like to shut with one of the crucial current and impactful works. Finish-to-Finish Adversarial Textual content-to-Speech by Deepmind.

EATS falls into the class of GAN-based TTS and is impressed by a earlier work referred to as GAN-TTS

EATS takes benefit of the adversarial coaching paradigm utilized in Generative Adversarial Networks. It operates on pure textual content or uncooked phoneme sequences and produces uncooked waveforms as outputs. EATS consists of two primary submodules: the aligner and the decoder.

The aligner receives the uncooked enter sequence and produces low-frequency aligned options in an summary function area. The aligner’s job is to map the unaligned enter sequence to a illustration that’s aligned with the output. The decoder takes the options and upsamples them utilizing 1D convolutions to provide audio waveforms. The entire system is skilled as a complete entity in an adversarial method

Supply: Finish-to-Finish Adversarial Textual content-to-Speech

Just a few key issues value mentioning are:

• The generator is a feed-forward neural community that makes use of a differentiable alignment scheme primarily based on token size prediction

• To permit the mannequin to seize temporal variation within the generated audio, mushy dynamic time warping can be employed.

As at all times for extra particulars, please advise the unique paper.

EATS achieved a MOS of 4.083

You too can discover an amazing rationalization of this structure by Yannic Kilcher, on his Youtube channel.

Conclusion

Textual content to speech is an space of analysis with plenty of novel concepts. It’s evident that the sector has come a great distance over the previous few years. Check out good units comparable to Google assistant, Amazon’s Alexa and Microsoft’s Cortana.

If you wish to experiment with a number of the above fashions, all it’s important to do is go into Pytorch’s or TensorFlow mannequin hub, discover your mannequin and mess around with it. One other nice useful resource is the next repo by Mozilla: TTS: Textual content-to-Speech for all. When you additionally need us to discover a distinct structure, be at liberty to ping us and we are able to embrace it right here as nicely.

References

• [1] Heiga Zen, Keiichi Tokuda, Alan W. Black, Statistical parametric speech synthesis, Speech Communication, Quantity 51, Subject 11, 2009
• [2] Aaron van den Oord et al., WaveNet: A Generative Mannequin for Uncooked Audio, arXiv:1609.03499, 2016
• [3] Sercan O. Arik et al., Deep Voice: Actual-time Neural Textual content-to-Speech, arXiv:1702.07825, 2017
• [4] Paine et al. , Quick Wavenet Technology Algorithm, arXiv:1611.09482v1, 2016
• [5] Arik et al., Deep Voice 2: Multi-Speaker Neural Textual content-to-Speech, arXiv:1705.08947, 2017
• [6] Ping et al., Deep Voice 3: Scaling Textual content-to-Speech with Convolutional Sequence Studying, arXiv:1710.07654, 2017
• [7] Yuxuan Wang et al., Tacotron: In direction of Finish-to-Finish Speech Synthesis, arXiv:1703.10135, 2017
• [8] Jonathan Shen et al., Pure TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions, arXiv:1712.05884, 2017
• [9] Aaron van den Oord et al., Parallel WaveNet: Quick Excessive-Constancy Speech Synthesis, arXiv:1711.10433, 2017
• [10] Yuxuan Wang et al., Type Tokens: Unsupervised Type Modeling, Management and Switch in Finish-to-Finish Speech Synthesis, arXiv:1803.09017, 2018
• [11] Naihan Li et al., Neural Speech Synthesis with Transformer Community, arXiv:1809.08895, 2018
• [12] Yi Ren et al., FastSpeech: Quick, Strong and Controllable Textual content to Speech, arXiv:1905.09263, 2019
• [13] Yi Ren et al., FastSpeech 2: Quick and Excessive-High quality Finish-to-Finish Textual content to Speech, arXiv:2006.04558, 2020
• [14] Ryan Prenger et al., WaveGlow: A Circulation-based Generative Community for Speech Synthesis, arXiv:1811.00002, 2018
• [15] Jeff Donahue et al., Finish-to-Finish Adversarial Textual content-to-Speech, arXiv:2006.03575, 2020

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