Audio transcription is the process of converting unstructured audio data, such as recordings of human speech, into structured data.
Artificial intelligence (AI) and machine learning (ML) algorithms require structured data to perform various tasks involving human speech, including speech recognition, sentiment analysis, and speaker identification. In short, audio transcription is fundamental for teaching computers to understand spoken language.
Prior to around 2015, audio transcription was entirely manual. You'd sit and listen to an audio recording and transcribe it manually by typing it out.
Today, computerized automatic speech recognition (ASR) models have streamlined the process of transcribing audio, enabling generations of AIs that understand spoken language. If you have an Alexa or equivalent, then you interact with ASR processes.
This article will explore audio transcription and its relationship with AI and data labeling.
Audio transcription has many practical uses, including in technologies such as Alexa, Google Assistant and thousands of other AIs that use speech recognition.
It’s worth mentioning that there are two broad forms of audio transcription – speech-to-text (STT) and text-to-speech (TTS).
STT converts verbal language, i.e., speech, into text, whereas TTS converts text back into speech. One is broadly analogous to listening (e.g., a machine listens to and understands your speech), and the other is broadly analogous to speaking (e.g., a machine speaks to you in natural language).
Here are some practical uses of audio transcription:
So, how do you actually go about transcribing audio?
Manual transcription involves human transcribers listening to audio recordings and typing the content. Until around 2015, manual transcription was the only way to transcribe spoken language into text reliably. It was a massive industry.
While manual transcription methods can produce highly accurate results, it’s time-consuming and expensive, making it non-feasible for large-scale projects. The computational need for audio data to build machine learning models eventually outgrew the capacity of manual transcription services.
ASR technology employs machine learning algorithms to transcribe audio data automatically.
ASR systems have made significant progress in recent years, with models like DeepSpeech and Wav2Vec offering impressive out-of-the-box performance. In addition, there are now many open-source Python tools and libraries for ASR.
Since it interacts with natural language, ASR falls into the natural language processing (NLP) subsection of AI, as below.
As ASR has developed, most of the main players in AI like Google, Amazon and OpenAI have staked their claims, including OpenAI’s Whisper, trained on 680,000 hours of multilingual and multitask supervised data.
While ASR has evolved to enable a whole host of advanced technologies like virtual assistants such as Alexa, there remain significant challenges in dealing with background noise, accents, slang, localized vernacular, and specialized vocabulary. Moreover, these models show maximum effectiveness for native English speakers and remain somewhat limited for multilingual use.
There are concerns that speech recognition technologies are biased, prejudiced, and inequitable.
Read our article Speech Recognition: Opportunities and Challenges.
ASR primarily sits within the supervised learning branch of machine learning. That means models are trained on training data, which teaches the model how to behave when exposed to new, unseen data. Read our Ultimate Guide for a detailed account of supervised machine learning.
To train ASR systems, vast amounts of labeled audio data are required. For example, Whisper is trained on 680,000 hours, or 77 years of continuous audio. You can see below how the vast majority of data was English, though they have included a vast range of languages in the training set.
The training process generally involves the following steps:
Competent data labeling plays a crucial role in training ASR systems, as high-quality transcriptions enable the model to learn more effectively and produce considerably more accurate speech results. Moreover, human-in-the-loop data labeling teams can pick up on issues in the training data and flag them prior to training.
Data labeling is a critical aspect of training speech recognition algorithms. The quality of a) the data and b) the labels directly impact the ASR system's performance.
Some key points to consider when labeling data for speech recognition include:
By prioritizing accurate and consistent data labeling, data scientists have a much better chance of training reliable ASR systems. While unsupervised techniques and reinforcement learning have both been applied to building ASR systems, supervised machine learning is the staple technique, thus necessitating data labeling.
Speech recognition algorithms have evolved, and many of the latest models are built using neural networking architecture and techniques.
Here’s a technical overview of modern ASR models.
Deep Neural Networks (DNNs) are feedforward networks composed of multiple layers of interconnected artificial neurons.
For ASR, these networks can learn complex, non-linear relationships between input features and output labels, which is crucial when dealing with enormous, complex datasets.
Convolutional Neural Networks (CNNs) are a specialized type of DNN that exploit the local structure of the input data, making them well-suited for tasks involving images or time-series data.
Recurrent Neural Networks (RNNs) are designed to model sequential data, making them ideal for speech recognition tasks.
One of the most popular RNN architectures used in ASR is the Long Short-Term Memory (LSTM) network.
Transformer models have shown remarkable success in various NLP tasks, including ASR.
These models rely on the self-attention mechanism, which allows them to capture dependencies across input sequences without relying on recurrent connections. OpenAI’s Whisper is a transformer model.
There are few ASR technologies as influential as Amazon Alexa, but how does it actually work?
As you might imagine, technologies like Alexa involve various machine learning algorithms and deep learning architectures. Some of the key technologies and techniques used in the Alexa system include:
Audio transcription is the process of converting spoken language in audio or video recordings into written text. It plays a vital role in various industries, including those we interact with on a near-daily basis.
Accurate labeled transcriptions provide the necessary information for developing and improving AI-driven speech recognition systems. Only then can we create more accurate and efficient AI models, improve accessibility, and optimize audio content analysis across numerous industries and sectors without bias or prejudice.
To launch your next audio transcription project, contact Aya Data for your data labeling needs.
Audio data labeling involves annotating audio files with relevant information, such as transcriptions, speaker identification, or other metadata. Labeled audio data enables the supervised training of machine learning models.
The best way to transcribe audio to text depends on the specific requirements, budget, and required accuracy. Options include manual transcription, where a human transcriber listens to the audio and types out the content, or automated transcription, where AI transcription services convert spoken language into text using speech recognition algorithms.
There are three broad approaches. The first is verbatim transcription, which captures every spoken word, including filler words (e.g., "um," "uh"), false starts, and repetitions. This is commonly used in legal settings, research, and psychological assessments.
In the second type, edited transcription, filler words, stutters, and repetitions are omitted, providing a clean and easy-to-read transcript.
In the third type, intelligent verbatim transcription, grammar, and syntax are refined to improve readability.