Foundation open-source models like Meta’s wav2vec2 [paper] and OpenAI’s Whisper [paper] have revolutionised speech-related tasks including automated speech recognition (ASR), speech emotion recognition (SER) and many more. They are trained on substantial data volumes of millions or even billions of annotated examples, making them robust models for many downstream and novel tasks in addition to the ones they were originally trained for.
However, to achieve optimal performance and high accuracy in real-world scenarios for specific tasks in specific settings, it is crucial to fine-tune these models. Part of our work at Maaind is this sort of optimisation of machine learning models to fit real world application spaces, such as at the job, in the car, and at home, with all of them having different requirements. This article explores the reasons why use-case specific models often outperform their open source counterparts, and how they can be used to improve machine learning in practical settings.
Training Data
Open source SER models are typically trained on generic datasets with clean and controlled speech data. However, use-case specific SER models can be trained on domain-specific and real-world datasets that include different languages, accents, background noises, and environmental conditions. This means that use-case specific models can capture a wider range of speech patterns and nuances, making them better suited for handling speech from diverse populations. Additionally, by training on real-world data, use-case specific models can also account for the variability in speech patterns that may not be present in clean and controlled datasets. This can lead to improved accuracy and performance in recognising speech in real-world settings.
Noise and Variability Handling
Noise is an important factor to consider in real-world applications, as it can easily worsen accuracy. Open source speech emotion recognition models may not be able to handle diverse and unexpected noise sources, which leads to reduced accuracy in real-world scenarios. Use-case specific models are optimised to handle specific noise profiles encountered in the intended application, resulting in improved robustness and accuracy in real-world and specific usecases.
Validation and Testing
Open source speech emotion recognition models may not have been extensively tested in diverse real-world scenarios and are usually trained and tested within the same data domain. At Maaind, we rigorously validate and test our AI models alongside our R&D process of NeuroLabs, where we use multimodal inputs in both lab-controlled and real-world experimental settings while co-recording with EEG, ECG and other modalities. We also do cross-corpus evaluation to ensure reliable performance for the intended use case.
Fairness and bias mitigation
An important point of criticism of machine learning models is the existence of biases towards certain genders, accents, or demographics. These biases can have negative impacts on the decisions made by the models, and can perpetuate or even amplify existing societal inequalities. Open source models, while widely available and accessible, often offer limited understanding and insight into how fair or unbiased they are. On the other hand, usecase-specific models, which are trained on specific data and tailored to particular scenarios, are generally better evaluated and tested for biases. This is because they are specifically designed to mitigate as much bias as possible when deployed in real world scenarios. By taking these factors into consideration, we can work towards creating machine learning models that are fair, unbiased, and ultimately more effective in serving their intended purposes.
Continuous performance optimisation
Open source models are quickly out of date if nobody provides continuous maintenance and updates to keep up with the latest technologies and trends. While open source models are generally considered trustworthy, it is important to recognise that they can still be vulnerable to corruption and other forms of malicious activity. This risk is present in all types of software development, including closed-source models. However, the advantage of closed-source models is that they are often well-maintained and updated regularly for optimal performance and robustness.
While open source models have played an important role in the advancement of machine learning, use-case specific models are essential for achieving optimal performance in real-world scenarios.
At Maaind, we are committed to provide accurate and practical models that improve the wellbeing of people. We work with companies and OEMs from a wide range of industries, providing them with solutions to make their products more emotionally intelligent. With our SER platform, products can understand the user’s emotional state from their voice and through #neuroadaptive learning suggest the right interventions and learn what works best for each user. It’s like a new language between technology and humans - not based on text but based on psychological and physiological states.
If you're interested in use-case specific models for your own applications, we invite you to reach out to anyone in the team. Follow us on LinkedIn to stay up-to-date with the newest developments in neuroscience, machine learning and #neuroadaptive AI.