Automatic speech recognition (ASR) is a cornerstone of many business applications in domains ranging from call centers to smart device engineering. At their core, ASR models, also referred to as Speech-to-Text (STT), intelligently recognize human speech and convert it into a written format.
Modern ASR engines leverage a combination of groundbreaking technologies, including Natural Language Processing (NLP), AI, ML, and LLMs. While ASR relies on all these technologies, it’s very different from each one of them in fundamental ways.
Take NLP, for example, which is frequently confused with ASR. The technology aims to understand natural language patterns and use those to create new content from the ground up, whereas ASR has a laser focus on transcribing the spoken word. It is therefore crucial to understand how speech recognition works to be able to build better products and experiences for your customers and teams.
In this article, we’ll explore the inner workings of ASR models and compare legacy approaches with today's state-of-the-art sequence-to-sequence models. If you’re a developer, AI engineer, CTO, or CPO, you’ll discover a wealth of insights into ASR models like Whisper seq2seq and how they’re vital for transcriptions, captioning, and content creation in business environments.
When incorporating speech recognition technologies into your business and customer workflows, it helps to understand how they work. Armed with these insights, you’ll be better able to select an ASR model that best meets your specific requirements. If you're a new to speech recognition, feel free to check out our introductory guide to speech-to-text AI before diving into this.
Let’s take a look at how speech recognition has historically worked. Legacy ASR systems, first introduced in the 1970s, function by converting analog audio signals into digital bits and processing them with a decoder to create sentences with words based on the data sequence and context. They remained mainstream for the last four decades until the introduction of end-to-end ASR models.
The decoder in a traditional speech recognition system analyzes the input data in conjunction with multiple ML models.
As you might have guessed, this approach necessitates training multiple models independently of each other, which is both time-consuming and expensive. One of the most significant drawbacks, however, is the reliance on the lexicon model.
The success of the traditional speech recognition models depends to a great extent on how well the lexicon models are crafted. Experienced phoneticians collaborate to create a custom set for the language at hand. The process must be repeated for every single language the model aims to support. This makes implementation challenging, especially in dynamic business environments expanding into new markets.
Modern ASR models take a disruptive approach to speech processing with end-to-end deep learning. One of the early approaches to building an end-to-end system was introduced in 2014 by researchers Alex Graves from Google DeepMind and Navdeep Jaitly from the University of Toronto.
Essentially, a complex neural network in a modern ASR model replaces multi-stage models in legacy systems which minimizes latency and drastically improves performance and accuracy.
This architecture also does away with independent language, acoustic, and lexicon models, and the resultant modern ASR system functions as a single neural network (as against multiple models in legacy systems), reducing development time and costs. In addition, they also achieve unparalleled accuracy levels and support multiple languages owing to their advanced neural architecture.
The modern ASR models are trained on large datasets and often require self-supervised deep learning as it would be extremely challenging for human operators to manually process voluminous data. Engineers use large amounts of unlabeled data to build a foundation model, which is then fine-tuned to achieve the desired word error rate (WER). Models can also be benchmarked based on this metric to compare performance.
A great example of a modern ASR model is OpenAI’s Whisper seq2seq model trained on a huge dataset of over 680,000 hours of audio data from many languages. This makes it more robust than models trained on smaller, more specialized datasets. The seq2seq model is essentially an ML architecture optimized for tasks that involve sequential data (like speech and text). It comprises two core components which take a sequence as input and generate a related sequence output.
We’ve summarized the key differences between the traditional and modern ASR approaches below for easy reference.
While legacy models suffer from drawbacks, they aren’t in any way rendered obsolete. In fact, modern ASR models build upon several traditional fundamental speech recognition systems such as acoustic and language models. Furthermore, legacy systems are also deployed for some specific tasks.
Generally speaking, though, Seq2seq models like Whisper are great at tasks that require natural language understanding. This includes speech recognition, translation, and caption generation. This is, in part, owing to their superior architecture that lends them advantages when dealing with sequences of varying lengths. That said, modern models do need very large datasets and compute resources to train and work. They can also face problems with long-range dependencies and when rare words are encountered.
We know how challenging it can be to select an optimal ASR model for your specific needs. We’ve put together a few decisive factors you should consider to streamline the selection process.
Automatic speech recognition plays a pivotal role in numerous business applications and knowing which kind of ASR system to use for a niche use case can greatly improve end-user experience.
Traditionally, legacy speech-to-text systems have used a combination of acoustic, lexicon, and language models to predict text. However, these suffer from accuracy constraints and require specialized expertise for phonetic training.
Modern ASR models are increasingly complex and can handle diverse input types, including multiple languages. A simplified end-to-end deep learning model ensures minimal latency, superior accuracy, and high scalability.
When choosing an ASR model for your business, consider its underlying architecture (such as RNN/transformer), input data modalities, and the desired word error rates.
Far-Field Automatic Speech Recognition (2020) by Reinhold Haeb-Umbach et al.
Speech-To-Text
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