Foundation models are integral to the world of artificial intelligence (AI). Combined with natural language processing (NLP), these technologies are changing the way machines understand and generate human language, leading to the development of powerful models that can perform a range of tasks.
Specialized models like foundation models and speech foundation models serve as the backbone of various NLP applications, making it possible to understand language in different contexts. While speech foundation models are tailored specifically for speech-specific tasks, the overall importance of these types of models can’t be overstated in driving AI’s capabilities forward.
In this blog post, we’ll give you an overview of both foundation models and speech foundation models, including their definitions, differences, applications, and how technology like aiOla is making the most of them.
What is a Foundation Model?
A foundation model is a large-scale AI deep-learning neural network trained on large amounts of diverse data. Scientists are using foundation models for machine learning (ML), using them as a jumping point for developing AI rather than starting from scratch every time.
Foundation models are capable of performing a range of general tasks like understanding language and conversing in natural language as well as generating images and text. Today, popular foundation models aren’t only for scientists but are being used by the public all the time. Language foundation models like OpenAI’s GPT and Google’s BERT can generate human-like text and serve as a starting point for many tasks.
There are many industries using foundation models to improve their work processes, such as healthcare for predictive diagnoses and customer service to help with personalized interactions. The technology has broad applications and the potential to transform processes in countless occupations.
Foundation Model: Key Characteristics
Distinguishing foundation models from other types of AI models is essential to get a deeper understanding of their capabilities. Foundation models can take on different forms, such as speech or language, making them versatile for various industries and applications. Still, they all possess similar characteristics, which we’ll take a look at below.
- Language understanding and generation capabilities: Foundation models excel in understanding and generating human-sounding language, interpreting context, and producing coherent and relevant text
- Extensive training data: For these models to work, they need to be trained on huge quantities of diverse data using different languages, styles, and topics
- Complex architecture: Foundation models rely on deep neural networks and transformer architectures with billions of parameters, making them complex enough to catch intricate patterns and relationships in input data
- Versatility: Due to this broad training, foundation models can be adapted to a range of needs and applications, from chatbots to virtual assistants and automated content generation
- Limitations and challenges: While they have many strengths, foundation models can be limited due to the need for significant computational resources as well as the possibility of biases in their training data, leading to both ethical and environmental concerns
Speech Foundation Models: What’s the Difference?
Speech foundation models are designed specifically to understand and generate spoken language. These models are trained using AI and large language-based datasets to learn how to accurately understand speech patterns, conversation, and language in context.
While foundation models focus primarily on written language, speech foundation models are tailored to handle the complexities of spoken language, including accent variations, intonations, and speech patterns. In essence, speech foundation models are what allow for more seamless human-computer interactions thanks to voice-activated technologies.
Foundation Models vs. Speech Foundation Models: Technical Differences
To go deeper into the difference between the traditional foundation model and speech foundation models, we have to go into the technical aspects that fuel the two. Here’s a breakdown of how the architecture, training, and performance metrics differ:
- Architecture: Foundation models handle text-based input and output with transformers designed for language processing, while speech foundation models add in audio processing layers like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to be able to convert audio signals into text
- Training data: When it comes to training, speech foundation models rely on both text and audio data to learn to accurately interpret and generate spoken language. In practice, this might look like training speech foundation models on datasets of recorded speech and the corresponding transcription, including different accents, languages, and styles
- Performance metrics: Traditional foundation models turn to metrics like Bilingual Evaluation Understudy (BLEU) to evaluate translation quality, while speech foundation models need to assess speech-specific metrics using Word Error Rate (WER) or Mean Opinion Score (MOS), helping ensure the models are understanding and generating language accurately
Use Cases and Applications of Speech Foundation Models
Both traditional foundation and speech foundation models are widely used in applications we use regularly today, such as chatbots, virtual assistants, or content generation systems. For example, chatbots are helping companies improve their customer service by accurately responding to customer queries and creating more personalized customer experiences.
Real-world applications of speech foundation models include:
- Voice assistants such as Amazon’s Alexa and Apple’s Siri, which use speech foundation models to interpret and respond to voice commands
- Speech recognition software that converts spoken language into text, such as Google Speech-to-text, uses speech foundation models and can help professionals in fields like law and healthcare document information with accurate transcription
- Transcription tools like Live Transcribe and Otter.ai use speech foundation models to power technologies that help individuals with hearing impairments with real-time transcription of spoken words, making speeches, meetings, or lectures more accessible
aiOla: Bringing Speech Technology to New Industries
As speech foundation models begin to power more and more applications, speech technology will become more accessible to different companies. aiOla, a speech AI technology, is being used in fields like manufacturing and logistics, fleet management, and food safety. With aiOla, traditional industries can harness the power of AI to bring innovation to various work processes.
Since it was trained on vast datasets, aiOla can understand over 100 languages, including different accents, dialects, and industry jargon. It can also be used in any acoustic environment with high levels of accuracy, making it fitting for noisy workplaces. Thanks to aiOla’s speech technology, companies have seen a whopping 90% reduction in manual operations, leading to more productive workflows, less wasted resources, and more efficient use of labor.
By pairing speech foundation models with advanced technologies like automatic speech recognition (ASR) and natural language understanding (NLU), aiOla can help teams reach new heights using language alone. As aiOla operates entirely through speech, teams can complete inspections hands-free, collect essential data just by speaking, forecast machinery malfunction, and complete and submit reports using voice.
Without a speech foundation model that was trained on huge quantities of speech and language data, vocal interactions between humans and machines would be less reliable, making it more challenging for traditional industries to innovate and grow as much as other more tech-driven fields.
Powering Innovation With Speech Foundation Models
Both foundation models and speech foundation models are moving the needle forward when it comes to building powerful AI applications that influence the way we work. While these models power AI tools like speech recognition and virtual assistants, they’ll likely only grow in capabilities as they have more data to train on and learn from. With aiOla, organizations can reap the benefits of these robust models to make their existing workflows more efficient, productive, and reliable.
Book a demo with one of our experts today to learn how aiOla can help you turn speech into action.
