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Posted by Joel Shor, Software Engineer and Oran Lang, Software Engineer, Google Research, Israel

There are many tasks within speech processing that are easier to solve by having large amounts of data. For example automatic speech recognition (ASR) translates spoken audio into text. In contrast, “non-semantic” tasks focus on the aspects of human speech other than its meaning, encompassing “paralinguistic” tasks, like speech emotion recognition, as well as other kinds of tasks, such as speaker identification, language identification, and certain kinds of voice-based medical diagnoses. In training systems to accomplish these tasks, one common approach is to utilize the largest datasets possible to help ensure good results. However, machine learning techniques that directly rely on massive datasets are often less successful when trained on small datasets.

One way to bridge the performance gap between large and small datasets is to train a representation model on a large dataset, then transfer it to a setting with less data. Representations can improve performance in two ways: they can make it possible to train small models by transforming high-dimensional data (like images and audio) to a lower dimension, and the representation model can also be used as pre-training. In addition, if the representation model is small enough to be run or trained on-device, it can improve performance in a privacy-preserving way by giving users the benefits of a personalized model where the raw data never leaves their device. While representation learning is commonly used in the text domain (e.g. BERT and ALBERT) and in the images domain (e.g. Inception layers and SimCLR), such approaches are underutilized in the speech domain.

Bottom:A large speech dataset is used to train a model, which is then rolled out to other environments. Top Left: On-device personalization — personalized, on-device models combine security and privacy. Top Middle: Small model on embeddings — general-use representations transform high-dimensional, few-example datasets to a lower dimension without sacrificing accuracy; smaller models train faster and are regularized. Top Right: Full model fine-tuning — large datasets can use the embedding model as pre-training to improve performance

Unambiguously improving generally-useful representations, for non-semantic speech tasks in particular, is difficult without a standard benchmark to compare “speech representation usefulness.” While the T5 framework systematically evaluates text embeddings and the Visual Task Adaptation Benchmark (VTAB) standardizes image embedding evaluation, both leading to progress in representation learning in those respective fields, there has been no such benchmark for non-semantic speech embeddings.

In “Towards Learning a Universal Non-Semantic Representation of Speech“, we make three contributions to representation learning for speech-related applications. First, we present a NOn-Semantic Speech (NOSS) benchmark for comparing speech representations, which includes diverse datasets and benchmark tasks, such as speech emotion recognition, language identification, and speaker identification. These datasets are available in the “audio” section of TensorFlow Datasets. Second, we create and open-source TRIpLet Loss network (TRILL), a new model that is small enough to be executed and fine-tuned on-device, while still outperforming other representations. Third, we perform a large-scale study comparing different representations, and open-source the code used to compute the performance on new representations.

A New Benchmark for Speech Embeddings
For a benchmark to usefully guide model development, it must contain tasks that ought to have similar solutions and exclude those that are significantly different. Previous work either dealt with the variety of possible speech-based tasks independently, or lumped semantic and non-semantic tasks together. Our work improves performance on non-semantic speech tasks, in part, by focusing on neural network architectures that perform well specifically on this subset of speech tasks.

The tasks were selected for the NOSS benchmark on the basis of their 1) diversity — they need to cover a range of use-cases; 2) complexity — they should be challenging; and 3) availability, with particular emphasis on those tasks that are open-source. We combined six datasets of different sizes and tasks.

Datasets for downstream benchmark tasks. *VoxCeleb results in our study were computed using a subset of the dataset that was filtered according to internal policy.

We also introduce three additional intra-speaker tasks to test performance in the personalization scenario. In some datasets with k speakers, we can create k different tasks consisting of training and testing on just a single speaker. Overall performance is averaged across speakers. These three additional intra-speaker tasks measure the ability of an embedding to adapt to a particular speaker, as would be necessary for personalized, on-device models, which are becoming more important as computation moves to smart phones and the internet of things.

To help enable researchers to compare speech embeddings, we’ve added the six datasets in our benchmark to TensorFlow Datasets (in the “audio” section) and open sourced the evaluation framework.

TRILL: A New State of the Art in Non-semantic Speech Classification
Learning an embedding from one dataset and applying it to other tasks is not as common in speech as in other modalities. However, transfer learning, the more general technique of using data from one task to help another (not necessarily with embeddings), has some compelling applications, such as personalizing speech recognizers and voice imitation text-to-speech from few samples. There have been many previously proposed representations of speech, but most of these have been trained on a smaller and less diverse data, have been tested primarily on speech recognition, or both.

To create a data-derived representation of speech that was useful across environments and tasks, we started with AudioSet, a large and diverse dataset that includes about 2500 hours of speech. We then trained an embedding model on a simple, self-supervised criteria derived from previous work on metric learning — embeddings from the same audio should be closer in embedding space than embeddings from different audio. Like BERT and the other text embeddings, the self-supervised loss function doesn’t require labels and only relies on the structure of the data itself. This form of self-supervision is the most appropriate for non-semantic speech, since non-semantic phenomena are more stable in time than ASR and other sub-second speech characteristics. This simple, self-supervised criteria captures a large number of acoustic properties that are leveraged in downstream tasks.

TRILL loss: Embeddings from the same audio are closer in embedding space than embeddings from different audio.

TRILL architecture is based on MobileNet, making it fast enough to run on mobile devices. To achieve high accuracy on this small architecture, we distilled the embedding from a larger ResNet50 model without performance degradation.

Benchmark Results
We compared the performance of TRILL against other deep learning representations that are not focused on speech recognition and were trained on similarly diverse datasets. In addition, we compared TRILL to the popular OpenSMILE feature extractor, which uses pre-deep learning techniques (e.g., a fourier transform coefficients, “pitch tracking” using a time-series of pitch measurements, etc.), and randomly initialized networks, which have been shown to be strong baselines. To aggregate the performance across tasks that have different performance characteristics, we first train a small number of simple models, for a given task and embedding. The best result is chosen. Then, to understand the effect that a particular embedding has across all tasks, we calculate a linear regression on the observed accuracies, with both the model and task as the explanatory variables. The effect a model has on the accuracy is the coefficient associated with the model in the regression. For a given task, when changing from one model to another, the resulting change in accuracy is expected to be the difference in y-values in the figure below.

Effect of model on accuracy.

TRILL outperforms the other representations in our study. Factors that contribute to TRILL’s success are the diversity of the training dataset, the large context window of the network, and the generality of the TRILL training loss that broadly preserves acoustic characteristics instead of prematurely focusing on certain aspects. Note that representations from intermediate network layers are often more generally useful. The intermediate representations are larger, have finer temporal granularity, and in the case of the classification networks they retain more general information that isn’t as specific to the classes on which they were trained.

Another benefit of a generally-useful model is that it can be used to initialize a model on a new task. When the sample size of a new task is small, fine-tuning an existing model may lead to better results than training the model from scratch. We achieved a new state-of-the-art result on three out of six benchmark tasks using this technique, despite doing no dataset-specific hyperparameter tuning.

To compare our new representation, we also tested it on the mask sub-challenge of the Interspeech 2020 Computational Paralinguistics Challenge (ComParE). In this challenge, models must predict whether a speaker is wearing a mask, which would affect their speech. The mask effects are sometimes subtle, and audio clips are only one second long. A linear model on TRILL outperformed the best baseline model, which was a fusion of many models on different kinds of features including traditional spectral and deep-learned features.

Summary
The code to evaluate NOSS is available on GitHub, the datasets are on TensorFlow Datasets, and the TRILL models are available on AI Hub.

The NOn-Semantic Speech benchmark helps researchers create speech embeddings that are useful in a wide range of contexts, including for personalization and small-dataset problems. We provide the TRILL model to the research community as a baseline embedding to surpass.

Acknowledgements
The core team behind this work includes Joel Shor, Aren Jansen, Ronnie Maor, Oran Lang, Omry Tuval, Felix de Chaumont Quitry, Marco Tagliasacchi, Ira Shavitt, Dotan Emanuel, and Yinnon Haviv. We’d also like to thank Avinatan Hassidim and Yossi Matias for technical guidance.

Posted by Yujin Tang, Research Software Engineer and David Ha, Staff Research Scientist, Google Research, Tokyo

Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight, and is a consequence of the selective attention that enables you to remain focused on important parts of the world without distraction from irrelevant details. It is believed that this selective attention mechanism enables people to condense broad sensory information into a form that is compact enough to be used for future decision making. While this may seem to be a limitation, such “bottlenecks” observed in nature can also inspire the design of machine learning systems that hope to mimic the success and efficiency of biological organisms. For example, while most methods presented in the deep reinforcement learning (RL) literature allow an agent to access the entire visual input, and even incorporating modules for predicting future sequences of visual inputs, perhaps reducing an agent’s access to its visual inputs via an attention constraint could be beneficial to an agent’s performance?

In our recent GECCO 2020 paper, “Neuroevolution of Self-Interpretable Agents” (AttentionAgent), we investigate the properties of such agents that employ a self-attention bottleneck. We show that not only are they able to solve challenging vision-based tasks from pixel inputs with 1000x fewer learnable parameters compared to conventional methods, they are also better at generalization to unseen modifications of their tasks, simply due to its ability to “not see details” that can confuse it. Furthermore, looking at where the agent is focusing its attention provides visual interpretability to its decision making process. The following diagram illustrates how the agent learned to deal with its attention bottleneck:

AttentionAgent learned to attend to task critical regions in its visual inputs. In a car driving task (CarRacing, top row), the agent mostly attends to the road borders, but shifts its focus to the turns before it changes heading directions. In a fireball dodging game (DoomTakeCover, bottom row), the agent focuses on fireballs and enemy monsters. Left: Visual inputs to the agent. Center: Agent’s attention overlaid on the visual inputs, the white patches indicate where the agent focuses its attention. Right: Visual cues based on which the agent makes decisions.

Agent with Artificial Attention
While there have been several works that explore how constraints such as sparsity may play a role in actually shaping the abilities of reinforcement learning agents, AttentionAgent takes inspiration from concepts related to inattentional blindness — when the brain is involved in effort-demanding tasks, it assigns most of its attention capacity only to task-relevant elements and is temporarily blind to other signals. To achieve this, we segment the input image into several patches and then rely on a modified self-attention architecture to simulate voting between patches to elect a subset to be considered important. The patches of interest are elected at each time step and, once determined, AttentionAgent makes decisions solely on these patches, ignoring the rest.

In addition to extracting key factors from visual inputs, the ability to contextualize these factors as they change in time is just as crucial. For example, a batter in the game of baseball must use visual signals to continuously keep track of the baseball’s location in order to predict its position and be able to hit it. In AttentionAgent, a long short-term memory (LSTM) model accepts information from the important patches and generates an action at each time step. LSTM keeps track of the changes in the input sequence, and can thus utilize the information to track how critical factors evolve over time.

It is conventional to optimize a neural network with backpropagation. However, because AttentionAgent contains non-differentiable operations for the generation of important patches, like sorting and slicing, it is not straightforward to apply such techniques for training. We therefore turn to derivative-free optimization algorithms to overcome this difficulty.

Overview of our method and illustration of data processing flow in AttentionAgent. Top: Input transformation — A sliding window segments an input image into smaller patches, and then “flattens” them for future processing. Middle: Patch election — The modified self-attention module holds votes between patches to generate a patch importance vector. Bottom: Action generation — AttentionAgent picks the patches of the highest importance, extracts corresponding features and makes decisions based on them.

Generalization to Unseen Modifications of the Environment
We demonstrate that Attention Agent learned to attend to a variety of regions in the input images. Visualization of the important patches provides a peek into how the agent is making decisions, illustrating that most selections make sense and are consistent with human intuition, and is a powerful tool for analyzing and debugging an agent in development. Furthermore, since the agent learned to ignore information non-critical to the core task, it can generalize to tasks where small environmental modifications are applied.

Here, we show that restricting the agent’s decision-making controller’s access to important patches only while ignoring the rest of the scene can result in better generalization, simply due to how the agent is restricted from “seeing things” that can confuse it. Our agent is trained to survive in the VizDoom TakeCover environment only, but it can also survive in unseen settings with higher walls, different floor textures, or when confronted with a distracting sign.

DoomTakeCover Generalization: The AttentionAgent is trained in the environment with no modifications (left). It is able to adapt to changes in the environment, such as a higher wall (middle, left), a different floor texture (middle, right), or floating text (right).

When one learns to drive during a sunny day, one also can transfer those skills (to some extent) to driving at night, on a rainy day, in a different car, or in the presence of bird droppings on the windshield. AttentionAgent is not only able to solve CarRacing-v0, it can also achieve similar performance in unseen conditions, such as brighter or darker scenery, or having its vision modified by artifacts such as side bars or background blobs, while requiring 1000x fewer parameters than conventional methods that fail to generalize.

CarRacing Generalization: No modification (left); color perturbation (middle, left); vertical bars on left and right (middle, right); added red blob (right).

Limitations and Future Work
While AttentionAgent is able to cope with various modifications of the environment, there are limitations to this approach, and much more work to be done to further enhance the generalization capabilities of the agent. For example, AttentionAgent does not generalize to cases where dramatic background changes are involved. The agent trained on the original car racing environment with the green grass background fails to generalize when the background is replaced with distracting YouTube videos. When we take this one step further and replace the background with pure uniform noise, we observe that the agent’s attention module breaks down and attends only to random patches of noise, rather than to the road-related patches. If we train an agent from scratch in the noisy background environment, it manages to get around the track, although the performance is mediocre. Interestingly, the agent still attends only to the noise, rather than to the road, it appears to have learned to drive by estimating where the lane is based on the number of selected patches on the left and right of the screen.

AttentionAgent fails to generalize to drastically modified environments. Left: The background suddenly becomes a cat (Creative Commons video). Middle: The background suddenly becomes an arcade game (Creative Commons video). Right: AttentionAgent learned to drive on pure noise background by avoiding noise patches.

The simplistic method we use to extract information from important patches may be inadequate for more complicated tasks. How we can learn more meaningful features, and perhaps even extract symbolic information from the visual input will be an exciting future direction. In addition to open sourcing the code to the research community, we have also released CarRacingExtension, a suite of car racing tasks that involve various environmental modifications, as testbeds and benchmark for ML researchers who are interested in agent generalizations.

Acknowledgements
This research was conducted by Yujin Tang, Duong Nguyen, and David Ha. We would like to thank Yingtao Tian, Lana Sinapayen, Shixin Luo, Krzysztof Choromanski, Sherjil Ozair, Ben Poole, Kai Arulkumaran, Eric Jang, Brian Cheung, Kory Mathewson, Ankur Handa, and Jeff Dean for valuable discussions.