Speech Recognition

A Dense Block is a module used in convolutional neural networks that connects all layers (with matching feature-map sizes) directly with each other. It was originally proposed as part of the DenseNet architecture. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers. In contrast to ResNets, we never combine features through summation before they are passed into a layer; instead, we combine features by concatenating them. Hence, the $\ell^{th}$ layer has $\ell$ inputs, consisting of the feature-maps of all preceding convolutional blocks. Its own feature-maps are passed on to all $L-\ell$ subsequent layers. This introduces $\frac{L(L+1)}{2}$ connections in an $L$-layer network, instead of just $L$, as in traditional architectures: "dense connectivity".

A Dilated Causal Convolution is a causal convolution where the filter is applied over an area larger than its length by skipping input values with a certain step. A dilated causal convolution effectively allows the network to have very large receptive fields with just a few layers.

WaveNet is an audio generative model based on the PixelCNN architecture. In order to deal with long-range temporal dependencies needed for raw audio generation, architectures are developed based on dilated causal convolutions, which exhibit very large receptive fields. The joint probability of a waveform $\vec{x} = \{ x_1, \dots, x_T \}$ is factorised as a product of conditional probabilities as follows: $$p\left(\vec{x}\right) = \prod_{t=1}^{T} p\left(x_t \mid x_1, \dots ,x_{t-1}\right)$$ Each audio sample $x_t$ is therefore conditioned on the samples at all previous timesteps.

Electric is an energy-based cloze model for representation learning over text. Like BERT, it is a conditional generative model of tokens given their contexts. However, Electric does not use masking or output a full distribution over tokens that could occur in a context. Instead, it assigns a scalar energy score to each input token indicating how likely it is given its context. Specifically, like BERT, Electric also models $p\_{\text {data }}\left(x\_{t} \mid \mathbf{x}\_{\backslash t}\right)$, but does not use masking or a softmax layer. Electric first maps the unmasked input $\mathbf{x}=\left[x\_{1}, \ldots, x\_{n}\right]$ into contextualized vector representations $\mathbf{h}(\mathbf{x})=\left[\mathbf{h}\_{1}, \ldots, \mathbf{h}\_{n}\right]$ using a transformer network. The model assigns a given position $t$ an energy score $$ E(\mathbf{x})\_{t}=\mathbf{w}^{T} \mathbf{h}(\mathbf{x})\_{t} $$ using a learned weight vector $w$. The energy function defines a distribution over the possible tokens at position $t$ as $$ p\_{\theta}\left(x\_{t} \mid \mathbf{x}_{\backslash t}\right)=\exp \left(-E(\mathbf{x})\_{t}\right) / Z\left(\mathbf{x}\_{\backslash t}\right) $$ $$ =\frac{\exp \left(-E(\mathbf{x})\_{t}\right)}{\sum\_{x^{\prime} \in \mathcal{V}} \exp \left(-E\left(\operatorname{REPLACE}\left(\mathbf{x}, t, x^{\prime}\right)\right)\_{t}\right)} $$ where $\text{REPLACE}\left(\mathbf{x}, t, x^{\prime}\right)$ denotes replacing the token at position $t$ with $x^{\prime}$ and $\mathcal{V}$ is the vocabulary, in practice usually word pieces. Unlike with BERT, which produces the probabilities for all possible tokens $x^{\prime}$ using a softmax layer, a candidate $x^{\prime}$ is passed in as input to the transformer. As a result, computing $p_{\theta}$ is prohibitively expensive because the partition function $Z\_{\theta}\left(\mathbf{x}\_{\backslash t}\right)$ requires running the transformer $|\mathcal{V}|$ times; unlike most EBMs, the intractability of $Z\_{\theta}(\mathbf{x} \backslash t)$ is more due to the expensive scoring function rather than having a large sample space.

CBHG is a building block used in the Tacotron text-to-speech model. It consists of a bank of 1-D convolutional filters, followed by highway networks and a bidirectional gated recurrent unit (BiGRU). The module is used to extract representations from sequences. The input sequence is first convolved with $K$ sets of 1-D convolutional filters, where the $k$-th set contains $C\_{k}$ filters of width $k$ (i.e. $k = 1, 2, \dots , K$). These filters explicitly model local and contextual information (akin to modeling unigrams, bigrams, up to K-grams). The convolution outputs are stacked together and further max pooled along time to increase local invariances. A stride of 1 is used to preserve the original time resolution. The processed sequence is further passed to a few fixed-width 1-D convolutions, whose outputs are added with the original input sequence via residual connections. Batch normalization is used for all convolutional layers. The convolution outputs are fed into a multi-layer highway network to extract high-level features. Finally, a bidirectional GRU RNN is stacked on top to extract sequential features from both forward and backward context.

Tacotron is an end-to-end generative text-to-speech model that takes a character sequence as input and outputs the corresponding spectrogram. The backbone of Tacotron is a seq2seq model with attention. The Figure depicts the model, which includes an encoder, an attention-based decoder, and a post-processing net. At a high-level, the model takes characters as input and produces spectrogram frames, which are then converted to waveforms.

HiFi-GAN is a generative adversarial network for speech synthesis. HiFi-GAN consists of one generator and two discriminators: multi-scale and multi-period discriminators. The generator and discriminators are trained adversarially, along with two additional losses for improving training stability and model performance. The generator is a fully convolutional neural network. It uses a mel-spectrogram as input and upsamples it through transposed convolutions until the length of the output sequence matches the temporal resolution of raw waveforms. Every transposed convolution is followed by a multi-receptive field fusion (MRF) module. For the discriminator, a multi-period discriminator (MPD) is used consisting of several sub-discriminators each handling a portion of periodic signals of input audio. Additionally, to capture consecutive patterns and long-term dependencies, the multi-scale discriminator (MSD) proposed in MelGAN is used, which consecutively evaluates audio samples at different levels.

Causal convolutions are a type of convolution used for temporal data which ensures the model cannot violate the ordering in which we model the data: the prediction $p(x_{t+1} | x_{1}, \ldots, x_{t})$ emitted by the model at timestep $t$ cannot depend on any of the future timesteps $x_{t+1}, x_{t+2}, \ldots, x_{T}$. For images, the equivalent of a causal convolution is a masked convolution which can be implemented by constructing a mask tensor and doing an element-wise multiplication of this mask with the convolution kernel before applying it. For 1-D data such as audio one can more easily implement this by shifting the output of a normal convolution by a few timesteps.

WaveRNN is a single-layer recurrent neural network for audio generation that is designed efficiently predict 16-bit raw audio samples. The overall computation in the WaveRNN is as follows (biases omitted for brevity): $$ \mathbf{x}\_{t} = \left[\mathbf{c}\_{t−1},\mathbf{f}\_{t−1}, \mathbf{c}\_{t}\right] $$ $$ \mathbf{u}\_{t} = \sigma\left(\mathbf{R}\_{u}\mathbf{h}\_{t-1} + \mathbf{I}^{*}\_{u}\mathbf{x}\_{t}\right) $$ $$ \mathbf{r}\_{t} = \sigma\left(\mathbf{R}\_{r}\mathbf{h}\_{t-1} + \mathbf{I}^{*}\_{r}\mathbf{x}\_{t}\right) $$ $$ \mathbf{e}\_{t} = \tau\left(\mathbf{r}\_{t} \odot \left(\mathbf{R}\_{e}\mathbf{h}\_{t-1}\right) + \mathbf{I}^{*}\_{e}\mathbf{x}\_{t} \right) $$ $$ \mathbf{h}\_{t} = \mathbf{u}\_{t} \cdot \mathbf{h}\_{t-1} + \left(1-\mathbf{u}\_{t}\right) \cdot \mathbf{e}\_{t} $$ $$ \mathbf{y}\_{c}, \mathbf{y}\_{f} = \text{split}\left(\mathbf{h}\_{t}\right) $$ $$ P\left(\mathbf{c}\_{t}\right) = \text{softmax}\left(\mathbf{O}\_{2}\text{relu}\left(\mathbf{O}\_{1}\mathbf{y}\_{c}\right)\right) $$ $$ P\left(\mathbf{f}\_{t}\right) = \text{softmax}\left(\mathbf{O}\_{4}\text{relu}\left(\mathbf{O}\_{3}\mathbf{y}\_{f}\right)\right) $$ where the $*$ indicates a masked matrix whereby the last coarse input $\mathbf{c}\_{t}$ is only connected to the fine part of the states $\mathbf{u}\_{t}$, $\mathbf{r}\_{t}$, $\mathbf{e}\_{t}$ and $\mathbf{h}\_{t}$ and thus only affects the fine output $\mathbf{y}\_{f}$. The coarse and fine parts $\mathbf{c}\_{t}$ and $\mathbf{f}\_{t}$ are encoded as scalars in $\left[0, 255\right]$ and scaled to the interval $\left[−1, 1\right]$. The matrix $\mathbf{R}$ formed from the matrices $\mathbf{R}\_{u}$, $\mathbf{R}\_{r}$, $\mathbf{R}\_{e}$ is computed as a single matrix-vector product to produce the contributions to all three gates $\mathbf{u}\_{t}$, $mathbf{r}\_{t}$ and $\mathbf{e}\_{t}$ (a variant of the GRU cell. $\sigma$ and $\tau$ are the standard sigmoid and tanh non-linearities. Each part feeds into a softmax layer over the corresponding 8 bits and the prediction of the 8 fine bits is conditioned on the 8 coarse bits. The resulting Dual Softmax layer allows for efficient prediction of 16-bit samples using two small output spaces (2 8 values each) instead of a single large output space (with 2 16 values).

Location Sensitive Attention is an attention mechanism that extends the additive attention mechanism to use cumulative attention weights from previous decoder time steps as an additional feature. This encourages the model to move forward consistently through the input, mitigating potential failure modes where some subsequences are repeated or ignored by the decoder. Starting with additive attention where $h$ is a sequential representation from a BiRNN encoder and ${s}\_{i-1}$ is the $(i − 1)$-th state of a recurrent neural network (e.g. a LSTM or GRU): $$ e\_{i, j} = w^{T}\tanh\left(W{s}\_{i-1} + Vh\_{j} + b\right) $$ where $w$ and $b$ are vectors, $W$ and $V$ are matrices. We extend this to be location-aware by making it take into account the alignment produced at the previous step. First, we extract $k$ vectors $f\_{i,j} \in \mathbb{R}^{k}$ for every position $j$ of the previous alignment $\alpha\_{i−1}$ by convolving it with a matrix $F \in R^{k\times{r}}$: $$ f\_{i} = F ∗ \alpha\_{i−1} $$ These additional vectors $f\_{i,j}$ are then used by the scoring mechanism $e\_{i,j}$: $$ e\_{i,j} = w^{T}\tanh\left(Ws\_{i−1} + Vh\_{j} + Uf\_{i,j} + b\right) $$

Mixture model network (MoNet) is a general framework allowing to design convolutional deep architectures on non-Euclidean domains such as graphs and manifolds. Image and description from: Geometric deep learning on graphs and manifolds using mixture model CNNs

Tacotron 2 is a neural network architecture for speech synthesis directly from text. It consists of two components: - a recurrent sequence-to-sequence feature prediction network with attention which predicts a sequence of mel spectrogram frames from an input character sequence - a modified version of WaveNet which generates time-domain waveform samples conditioned on the predicted mel spectrogram frames In contrast to the original Tacotron, Tacotron 2 uses simpler building blocks, using vanilla LSTM and convolutional layers in the encoder and decoder instead of CBHG stacks and GRU recurrent layers. Tacotron 2 does not use a “reduction factor”, i.e., each decoder step corresponds to a single spectrogram frame. Location-sensitive attention is used instead of additive attention.

WaveGlow is a flow-based generative model that generates audio by sampling from a distribution. Specifically samples are taken from a zero mean spherical Gaussian with the same number of dimensions as our desired output, and those samples are put through a series of layers that transforms the simple distribution to one which has the desired distribution.

FastSpeech2 is a text-to-speech model that aims to improve upon FastSpeech by better solving the one-to-many mapping problem in TTS, i.e., multiple speech variations corresponding to the same text. It attempts to solve this problem by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e.g., pitch, energy and more accurate duration) as conditional inputs. Specifically, in FastSpeech 2, we extract duration, pitch and energy from speech waveform and directly take them as conditional inputs in training and use predicted values in inference. The encoder converts the phoneme embedding sequence into the phoneme hidden sequence, and then the variance adaptor adds different variance information such as duration, pitch and energy into the hidden sequence, finally the mel-spectrogram decoder converts the adapted hidden sequence into mel-spectrogram sequence in parallel. FastSpeech 2 uses a feed-forward Transformer block, which is a stack of self-attention and 1D-convolution as in FastSpeech, as the basic structure for the encoder and mel-spectrogram decoder.

Iterative Pseudo-Labeling (IPL) is a semi-supervised algorithm for speech recognition which efficiently performs multiple iterations of pseudo-labeling on unlabeled data as the acoustic model evolves. In particular, IPL fine tunes an existing model at each iteration using both labeled data and a subset of unlabeled data.

Local Prior Matching is a semi-supervised objective for speech recognition that distills knowledge from a strong prior (e.g. a language model) to provide learning signal to a discriminative model trained on unlabeled speech. The LPM objective minimizes the cross entropy between the local prior and the model distribution, and is minimized when $q\_{y\mid{x}} = \bar{p}\_{y\mid{x}}$. Intuitively, LPM encourages the ASR model to assign posterior probabilities proportional to the linguistic probabilities of the proposed hypotheses.

MelGAN is a non-autoregressive feed-forward convolutional architecture to perform audio waveform generation in a GAN setup. The architecture is a fully convolutional feed-forward network with mel-spectrogram $s$ as input and raw waveform $x$ as output. Since the mel-spectrogram is at a 256× lower temporal resolution, the authors use a stack of transposed convolutional layers to upsample the input sequence. Each transposed convolutional layer is followed by a stack of residual blocks with dilated convolutions. Unlike traditional GANs, the MelGAN generator does not use a global noise vector as input. To deal with 'checkerboard artifacts' in audio, instead of using PhaseShuffle, MelGAN uses kernel-size as a multiple of stride. Weight normalization is used for normalization. A window-based discriminator, similar to a PatchGAN is used for the discriminator.

E-BRANCHFORMER: BRANCHFORMER WITH ENHANCED MERGING FOR SPEECH RECOGNITION

Bridge-net is an audio model block used in the ClariNet text-to-speech architecture. Bridge-net maps frame-level hidden representation to sample-level through several convolution blocks and transposed convolution layers interleaved with softsign non-linearities.

ClariNet is an end-to-end text-to-speech architecture. Unlike previous TTS systems which use text-to-spectogram models with a separate waveform synthesizer (vocoder), ClariNet is a text-to-wave architecture that is fully convolutional and can be trained from scratch. In ClariNet, the WaveNet module is conditioned on the hidden states instead of the mel-spectogram. The architecture is otherwise based on Deep Voice 3.

WaveGrad is a conditional model for waveform generation through estimating gradients of the data density. This model is built on the prior work on score matching and diffusion probabilistic models. It starts from Gaussian white noise and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram. WaveGrad is non-autoregressive, and requires only a constant number of generation steps during inference. It can use as few as 6 iterations to generate high fidelity audio samples.

WaveGrad DBlocks are used to downsample the temporal dimension of noisy waveform in WaveGrad. They are similar to UBlocks except that only one residual block is included. The dilation factors are 1, 2, 4 in the main branch. Orthogonal initialization is used.

Big-Little Modules are blocks for image models that have two branches: each of which represents a separate block from a deep model and a less deep counterpart. They were proposed as part of the BigLittle-Net architecture. The two branches are fused with a linear combination and unit weights. These two branches are known as Big-Branch (more layers and channels at low resolutions) and Little-Branch (fewer layers and channels at high resolution).

VoiceFilter-Lite is a single-channel source separation model that runs on the device to preserve only the speech signals from a target user, as part of a streaming speech recognition system. In this architecture, the voice filtering model operates as a frame-by-frame frontend signal processor to enhance the features consumed by the speech recognizer, without reconstructing audio signals from the features. The key contributions are (1) A system to perform speech separation directly on ASR input features; (2) An asymmetric loss function to penalize oversuppression during training, to make the model harmless under various acoustic environments, (3) An adaptive suppression strength mechanism to adapt to different noise conditions.

Big-Little Net is a convolutional neural network architecture for learning multi-scale feature representations. This is achieved by using a multi-branch network, which has different computational complexity at different branches with different resolutions. Through frequent merging of features from branches at distinct scales, the model obtains multi-scale features while using less computation. It consists of Big-Little Modules, which have two branches: each of which represents a separate block from a deep model and a less deep counterpart. The two branches are fused with linear combination + unit weights. These two branches are known as Big-Branch (more layers and channels at low resolutions) and Little-Branch (fewer layers and channels at high resolution).

Conditional DBlock is a residual based block used in the discriminator of the GAN-TTS architecture. They are similar to the GBlocks used in the generator, but without batch normalization. Unlike the DBlock, the Conditional DBlock adds the embedding of the linguistic features after the first convolution.

DBlock is a residual based block used in the discriminator of the GAN-TTS architecture. They are similar to the GBlocks used in the generator, but without batch normalisation.

GAN-TTS is a generative adversarial network for text-to-speech synthesis. The architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyze the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced. The generator architecture consists of several GBlocks, which are residual based (dilated) convolution blocks. GBlocks 3–7 gradually upsample the temporal dimension of hidden representations by factors of 2, 2, 2, 3, 5, while the number of channels is reduced by GBlocks 3, 6 and 7 (by a factor of 2 each). The final convolutional layer with Tanh activation produces a single-channel audio waveform. Instead of a single discriminator, GAN-TTS uses an ensemble of Random Window Discriminators (RWDs) which operate on randomly sub-sampled fragments of the real or generated samples. The ensemble allows for the evaluation of audio in different complementary ways.

GBlock is a type of residual block used in the GAN-TTS text-to-speech architecture - it is a stack of two residual blocks. As the generator is producing raw audio (e.g. a 2s training clip corresponds to a sequence of 48000 samples), dilated convolutions are used to ensure that the receptive field of $G$ is large enough to capture long-term dependencies. The four kernel size-3 convolutions in each GBlock have increasing dilation factors: 1, 2, 4, 8. Convolutions are preceded by Conditional Batch Normalisation, conditioned on the linear embeddings of the noise term $z \sim N\left(0, \mathbf{I}\_{128}\right)$ in the single-speaker case, or the concatenation of $z$ and a one-hot representation of the speaker ID in the multi-speaker case. The embeddings are different for each BatchNorm instance. A GBlock contains two skip connections, the first of which in GAN-TTS performs upsampling if the output frequency is higher than the input, and it also contains a size-1 convolution if the number of output channels is different from the input.

Multiple Random Window Discriminator is a discriminator used for the GAN-TTS text-to-speech architecture. These discriminators operate on randomly sub-sampled fragments of the real or generated samples. The ensemble allows for the evaluation of audio in different complementary ways, and is obtained by taking a Cartesian product of two parameter spaces: (i) the size of the random windows fed into the discriminator; (ii) whether a discriminator is conditioned on linguistic and pitch features. For example, in the authors' best-performing model, they consider five window sizes (240, 480, 960, 1920, 3600 samples), which yields 10 discriminators in total. Using random windows of different size, rather than the full generated sample, has a data augmentation effect and also reduces the computational complexity of RWDs. In the first layer of each discriminator, the MRWD reshapes (downsamples) the input raw waveform to a constant temporal dimension $\omega = 240$ by moving consecutive blocks of samples into the channel dimension, i.e. from $\left[\omega\_{k}, 1\right]$ to $\left[\omega, k\right]$, where $k$ is the downsampling factor (e.g. $k = 8$ for input window size $1920$). This way, all the RWDs have the same architecture and similar computational complexity despite different window sizes. The conditional discriminators have access to linguistic and pitch features, and can measure whether the generated audio matches the input conditioning. This means that random windows in conditional discriminators need to be aligned with the conditioning frequency to preserve the correspondence between the waveform and linguistic features within the sampled window. This limits the valid sampling to that of the frequency of the conditioning signal (200Hz, or every 5ms). The unconditional discriminators, on the contrary, only evaluate whether the generated audio sounds realistic regardless of the conditioning. The random windows for these discriminators are sampled without constraints at full 24kHz frequency, which further increases the amount of training data. For the architecture, the discriminators consists of blocks (DBlocks) that are similar to the GBlocks used in the generator, but without batch normalisation. Unconditional RWDs are composed entirely of DBlocks. In conditional RWDs, the input waveform is gradually downsampled by DBlocks, until the temporal dimension of the activation is equal to that of the conditioning, at which point a conditional DBlock is used. This joint information is then passed to the remaining DBlocks, whose final output is average-pooled to obtain a scalar. The dilation factors in the DBlocks’ convolutions follow the pattern 1, 2, 1, 2 – unlike the generator, the discriminator operates on a relatively small window, and the authors did not observe any benefit from using larger dilation factors.

PASE+ is a problem-agnostic speech encoder that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). An online speech distortion module is employed, that contaminates the input signals with a variety of random disturbances. A revised encoder is also proposed that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, the authors refine the set of workers used in self-supervision to encourage better cooperation.

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