Using adversarial training to recognize speakers’ emotions

A person’s tone of voice can tell you a lot about how they’re feeling. Not surprisingly, emotion recognition is an increasingly popular conversational-AI research topic.

Emotion recognition has a wide range of applications: it can aid in health monitoring; it can make conversational-AI systems more engaging; and it can provide implicit customer feedback that could help voice agents like Alexa learn from their mistakes.

Typically, emotion classification systems are neural networks trained in a supervised fashion: training data is labeled according to the speaker’s emotional state, and the network learns to predict the labels from the data. At this year’s International Conference on Acoustics, Speech, and Signal Processing, my colleagues and I presented an alternative approach, in which we used a publicly available data set to train a neural network known as an adversarial autoencoder.

An adversarial autoencoder is an encoder-decoder neural network: one component of the network, the encoder, learns to produce a compact representation of input speech; the decoder reconstructs the input from the compact representation. The adversarial learning forces the encoder’s representations to conform to a desired probability distribution.

The compact representation — or “latent” representation — encodes all properties of the training example. In our model, we explicitly dedicate part of the latent representation to the speaker’s emotional state and assume that the remaining part captures all other input characteristics.

Our latent emotion representation consists of three network nodes, one for each of three emotional measures: valence, or whether the speaker’s emotion is positive or negative; activation, or whether the speaker is alert and engaged or passive; and dominance, or whether the speaker feels in control of the situation. The remaining part of the latent representation is much larger, 100 nodes.

The architecture of our adversarial autoencoder. The latent representation has two components (emotion classes and style), whose outputs feed into two adversarial discriminators.

We conduct training in three phases. In the first phase, we train the encoder and decoder using data without labels. In the second phase, we use adversarial training to tune the encoder.

Each latent representation — the three-node representation and the 100-node representation — passes to an adversarial discriminator. The adversarial discriminators are neural networks that attempt to distinguish real data representations, produced by the encoder, from artificial representations generated in accord with particular probability distributions. The encoder, in turn, attempts to fool the adversarial discriminator.

In so doing, the encoder learns to produce representations that fit the probability distributions. This ensures that it will not overfit the training data, or rely too heavily on statistical properties of the training data that don’t represent speech data in general.

In the third phase, we tune the encoder to ensure that the latent emotion representation predicts the emotional labels of the training data. We repeat all three training phases until we converge on the model with the best performance.

For training, we used a public data set containing 10,000 utterances from 10 different speakers, labeled according to valence, activation, and dominance. We compared the performance of the proposed learning method and the fully supervised learning baseline and observed marginal improvements.

In tests in which the inputs to our network were sentence-level feature vectors hand-engineered to capture relevant information about a speech signal, our network was 3% more accurate than a conventionally trained network in assessing valence.

When the input to the network was a sequence of vectors representing the acoustic characteristics of 20-millisecond frames, or audio snippets, the improvement was 4%. This suggests that our approach could be useful for end-to-end spoken-language-understanding systems, which dispense with hand-engineered features and rely entirely on neural networks.

Moreover, unlike conventional neural nets, adversarial autoencoders can benefit from training with unlabeled data. In our tests, for purposes of benchmarking, we used the same data sets to train both our network and the baseline network. But it’s likely that using additional unlabeled data in the first and second training phases can improve the network’s performance.

About the Author
Viktor Rozgic is a senior applied scientist in the Alexa Speech group.

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Want to build the future of music and audio entertainment?Imagine being part of an agile team, where your ideas have the potential to reach millions. Envision working within a startup atmosphere, while being able to leverage the resources of a Fortune-500 company. Picture working on bleeding-edge consumer-facing products, where every team member is a critical voice in the decision-making process. Welcome to Amazon Music’s New Projects team.Our team builds new experiences for Amazon Music listeners. We help our customers discover up-and-coming creators, while also having access to their favorite music and podcasts. We build systems that are distributed around the world, spanning our music apps, web player, and voice-forward experiences on mobile and Amazon Echo devices, powered by Alexa. Amazon Music products support our mission of delivering audio entertainment in new and exciting ways that listeners love.Amazon Music’s New Projects team is looking for founding team members across a variety of functions, including software engineering/development, product, marketing, design, and more. Come make history, as we launch new projects for millions of listeners.
US, MA, Boston
Data Scientist: Elastic Block Store Data Services (AWS)Come and build the future with us as we change the way customers move and transform their EBS data at never before seen scale.EBS customers frequently need to move or transform their underlying data whether its accelerating volume creation from snapshots using Fast Snapshot Restore, increasing their database storage or changing their volume type through Elastic volumes, or encrypting their volumes using AWS managed keys. Our team creates solutions that enable and simplify EBS customer workflows.Building a High-Performing & Inclusive Team CultureYou should be passionate about working with a world-class team that welcomes, celebrates, and leverages a diverse set of backgrounds and skillsets to deliver results. Driving results is your primary responsibility, and doing so in a way that builds on our inclusive culture is key to our long term success.Work/Life BalanceEBS Data Services values work-life balance. On normal days, our entire team is co-located in the Boston office, but we’re also flexible when people occasionally need to work from home. We generally keep core available hours from 10am to 4pm. Some of the team is available earlier and the rest of us work a little later.Energizing and Interesting Technical ProblemsYou will work in partnership with engineers on the team to build and operate large scale systems that move and transform customer volume data and accelerate access to their data. You’ll be working to provide solutions to both internal and external customers and engage deeply with other teams within EBS, S3, EC2, and many other services. It’s humbling and energizing to provide data movement solutions to customers at AWS scale.Mentorship & Career GrowthWe’re committed to the growth and development of every member of EBS Data Services, and that includes our engineers. You will have the opportunity to contribute to the culture and direction of the entire EBS org and deliver initiatives that will improve the life of all of our teams.EBS Data Services is a growth environment - we’re hiring and scaling rapidly to meet the needs of our customers. You’ll have the opportunity to grow your scope of influence naturally as we scale and work on solutions that impact some of Amazon's largest customers.
US, MA, Virtual Location - Massachuset
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun.The Research and Advanced Development team at Amazon Robotics is seeking interns with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, and planning/scheduling. You will be challenged intellectually and have a good time while you are at it!