The emergence of virtual assistants such as Siri and Alexa has made automatic speech recognition systems more widely used and developed. Automatic Speech Recognition (ASR) is a process of converting spoken words into text. The technology is being used in instant messaging applications, search engines, in-vehicle systems and home automation. Although all of these systems rely on slightly different technical processes, the first step in all of these systems is the same: capturing speech data and converting it into machine-readable text.
But how does the ASR system work? How does it learn to recognize speech? ASR systems: how do they work? So, at a basic level, we know that automatic speech recognition looks like this: audio data in, text data out. However, from input to output, audio data needs to be turned into machine-readable data. This means that data is sent through an acoustic Model and a language model. The two processes go like this: an acoustic model determines the relationship between audio signals and phonetic units in a language, and a language model matches sounds to words and word sequences.
These two models allow ASR systems to probabilistically examine audio input to predict words and sentences in it. The system then selects the prediction with the highest confidence level. **Sometimes a language model can prioritize certain predictions that are considered more likely due to other factors. So if you run a phrase through an ASR system, it will do the following: Take a voice input: “Hey Siri, what time is it?” Run the speech data through an acoustic model, breaking it down into speech parts. Run that data through a language model. Output text data: “Hey Siri, what time is it?”
Here, it is worth mentioning that if an automatic speech recognition system is part of a speech user interface, the ASR model will not be the only machine learning model running. Many automatic speech recognition systems work with natural language processing (NLP) and text-to-speech (TTS) systems to perform their given roles. That said, delving into voice user interfaces is an entire topic in itself. To learn more, check out this article.
So, now that you know how an ASR system works, what needs to be built? The key is data. Building an ASR system: The importance of data, a good ASR system should have flexibility. It needs to recognize a wide variety of audio input (speech samples) and make accurate textual output based on that data so that it can react accordingly. To achieve this, the data required by the ASR system are labeled speech samples and transcriptions. It’s a bit more complicated than that (eg, the data labeling process is very important and often overlooked), but it’s simplified here for the sake of clarity.
ASR systems require a large amount of audio data. why? Because language is complicated. There are many ways of saying the same thing, and the meaning of the sentence changes with the position and emphasis of the word. Also take into account that there are many different languages in the world where pronunciation and word choice can vary based on factors such as geographic location and accent.
Oh, and don’t forget that languages also vary by age and gender! With this in mind, the more speech samples an ASR system is given, the better it will be at recognizing and classifying new speech input. The more samples taken from a wide variety of sounds and environments, the better the system can recognize sounds in those environments. With dedicated fine-tuning and maintenance, the automatic speech recognition system will improve over the course of its use.
So, from the most basic point of view, more data is better. It is true that the current research is related to optimizing smaller datasets, but most models today still require large amounts of data to perform well. Fortunately, audio data collection has never been easier thanks to dataset repositories and dedicated data collection services. This in turn increases the speed of technological development, so let’s take a brief look at where automatic speech recognition can play a big role in the future.
ASR technology has been integrated into society. Virtual assistants, in-vehicle systems and home automation are all making everyday life easier and potentially expanding. Technology will further develop as more people embrace these services.