In this article, you’ll discover how using a solution like Happy Scribe’s AI- and human-assisted transcription can drastically cut down production time while boosting the quality of your transcriptions. Learn how accuracy in ASR (Automatic Speech Recognition) is measured, what factors influence it, and how every improvement translates to time savings and better content workflows.
Automatic speech recognition (ASR) technology has come a long way. It has improved in accuracy, speed, and versatility, allowing users to benefit from it in applications that range from the ease of mobile device dictation to video transcription for video editing workflows or transcribing real-life meetings.
So, it's clear that AI transcription accuracy is important if you don't want to spend hours and hours perfecting the transcribed file, but how is accuracy actually measured? Let's dive in.
Accuracy in ASR (Automatic Speech Recognition) means how closely the text matches what was actually spoken. Even a small increase in accuracy saves time in the editing process, for example.
ASR accuracy is measured using different methods. The most well-known is the “Word Error Rate (WER),” which shows how much the automatically generated transcript differs from a human-verified one, word by word. It is basically a way to quantify the difference between an automatically generated transcript and the corresponding human-verified text at the word level.
WER captures the number of errors: insertions, deletions, and substitutions made by an ASR system against the total number of words in the resulting text. By providing a percentage-based representation of these errors, WER offers a standardized way to compare the accuracy of different ASR models and works as a type of general ASR accuracy assessment.
The formula for calculating WER is as follows, with:
S = Number of word substitutions
D = Number of word deletions
I = Number of word insertions
N = Total number of words in the reference text
For example, in a 4,000-word transcript where a human reviewer corrected three named entities (substitution of proper nouns like people names and brands), deleted 24 words, and inserted 9, the resulting WER is 0.9, or 99.1% accuracy.
Happy Scribe bases AI transcription accuracy by the amount of effort required by a human reviewer to make the AI-generated output error-free. Simply put, on a scale of 1-100%: 85% means that 14% of the output requires editing. The less effort needed to correct the AI output, the faster and more cost-effective the process becomes.
Even when transcription users opt for human transcription, fewer edits result in faster deliveries and reduced costs. Combined with human revision, accuracy levels jump to 94-99%, the highest in the industry according to daily tests on 1,000 recordings. For users concerned about compliance, this equals peace of mind.
AI accuracy directly benefits your workflow by reducing the time spent fixing errors. For instance, if Happy Scribe’s accuracy means saving 20% of the time compared to using another transcription engine, this time-saving is quite significant in day-to-day operations, with less time spent on editing and more time focused on creating quality content. Happy Scribe measures AI accuracy on a daily basis.
Another commonly used metric is “Character Error Rate (CER).” CER provides a more granular assessment of ASR performance, compared to the Word Error Rate (WER) metric, and it is calculated as the percentage of incorrect characters relative to the total number of characters in the output text.
CER is particularly useful for languages with complex writing systems or where even minor character-level errors can significantly impact meaning. For such languages, CER provides a more detailed evaluation of ASR accuracy, highlighting subtle errors that might otherwise go unnoticed in a WER assessment. Examples include Chinese and Japanese, where a single character can represent a whole word, and Turkish and Finnish, where many words are formed by a combination of morphemes (the smallest linguistic elements with meaning).
The formula for calculating CER is almost identical to the one for calculating WER, except that instead of words, characters are measured.
Another way to measure accuracy in ASR has that very word in the metric’s name: “Accuracy Rate (AR).” AR quantifies the overall correctness of a transcription by measuring the percentage of words or characters that are accurately recognized.
AR is calculated by dividing the number of correctly recognized words or characters by the total number of words or characters in the output text. As the name implies, this metric is a straightforward and easily understandable measure of ASR performance. It is mostly used as a complement to more detailed error-based metrics like WER or CER, or to establish a general baseline for the performance of ASR systems and track their progress over time.
Other ways to assess the effectiveness of ASR systems, but less known than WER, CER, and AR, are “precision,” “recall,” and “F1 Score.”
Precision focuses on the quality of the ASR output, measuring the proportion of correctly recognized words or named entities among all the words or entities identified by the system. For example, if an ASR system correctly identifies eight out of ten named entities in a transcript while incorrectly labeling two other words as named entities, its precision value for named entity recognition would be 80%.
Recall emphasizes the completeness of the ASR system's output. It measures the proportion of correctly recognized words or named entities among all the actual words or entities present in the reference text. Using the previous example, if the actual number of named entities was 12, the recall of the ASR system would be 66.67% (eight out of 12).
The F1 score serves as a mean value between precision and recall. It is considered a way to obtain a balanced assessment of an ASR system taking into account other metrics, such as precision and recall. A high F1 score signals that the ASR system transcription is both accurate (high precision) and complete (high recall).
Several factors can influence how well ASR systems decode and capture human speech. They can range from the quality of the audio itself to the complexities of language and context.
Audio quality is perhaps the most important element impacting ASR performance. Ideally, all recordings are crystal-clear and free from background noise and distortions, but that is often not the case. When audio is plagued by muffled voices, competing sounds, echoes, reverberations, and other anomalies, accuracy might be significantly reduced.
Speaker variability, as in different accents, dialects, vocal patterns, tone, and volume, can be a challenge for ASR systems, even if well structured to handle most of them. For example, a Scottish brogue might confound a system trained primarily on American English, while speakers with poor enunciation and rapid-fire speaking patterns could challenge even the most sophisticated ASR models.
Vocabulary and models, more precisely the size and the ASR system's vocabulary and language model, contribute in great measure to its accuracy. Larger vocabulary can prepare the ASR system to recognize a wider range of words, and a robust language model helps it to better predict and interpret the likelihood of word sequences and grammatical structures. This can lead to more accurate transcriptions, particularly in specialized domains with technical jargon or complex sentence structures.
Domain-specific model training can further improve the ASR system accuracy. For example, a model fine-tuned on medical terminology should more accurately transcribe a doctor's dictation than one that is not. Targeted model training enables the system to grasp domain-specific and contextual nuances and terminology, which enhances its accuracy within that domain and context. This type of training can mark the difference between the system correctly recognizing a term like “myocardial infarction” instead of “myocardial infection.”
Happy Scribe uses a combination of public datasets alongside proprietary data to train and evaluate the AI models. The key to Happy Scribe’s accuracy lies in this proprietary data, which consists of real user-uploaded content used for training with their permission.
This vast and diverse dataset is what sets Happy Scribe apart, enabling the company to fine-tune the models to handle a wide range of use cases effectively. By incorporating data with various accents, dialects, and languages, the models become more robust and capable of accurately transcribing diverse audio content.
Happy Scribe’s approach to accuracy is consistent across transcription, subtitling, and translation. While it is challenging to compare directly with other companies offering these services due to the proprietary nature of their models, what customers can attest to is that Happy Scribe’s AI saves significant time in editing. For example, fixing one hour of audio might take two hours with Happy Scribe’s technology vs two and a half hours with other providers.
Improving ASR accuracy is possible through a set of proven approaches. Keeping in mind that the foundation of any successful ASR system lies in the quality and diversity of its training data, having quality data is the first step in ensuring accuracy outputs.
This starts with quality audio recordings, ideally representing a variety of environments and featuring speakers with diverse accents, dialects, and speech patterns. These data enable the ASR system to “learn and adapt” to the complexities of human speech. Of course, it helps if the audio can be first cleaned up to minimize things like background noise and sound anomalies.
A next essential step in improving ASR accuracy is human review. A well-structured evaluation process involving a human reviewer can help with error identification and correction of these errors, which in turn enables model fine-tuning. Human reviewers can also identify subtle nuances in speech that may escape automated algorithms, such as a characteristic speech affection.
Improving accuracy is not a matter of a one-time intervention. In fact, it is a continuous, consistent effort that requires regular testing and evaluation. These tests and evaluations should include automated metrics and human review, thereby refining the model based on new data and user feedback.
Fine-tuning a model to suit specific use cases or environments will also improve ASR performance in those cases. For example, a model designed for transcribing medical dictations may benefit from additional training on medical and insurance codes and acronyms. And a model designed for news street-interviews can be optimized to filter out background noise and focus on speaker voices.
As ASR technology gets better and better, understanding the different ways to measure accuracy and adopting best practices in all processes to improve it will help users unlock its full potential, regardless of setting, language, and subject matter.
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In this article, you’ll discover how using a solution like Happy Scribe’s AI- and human-assisted transcription can drastically cut down production time while boosting the quality of your transcriptions. Learn how accuracy in ASR (Automatic Speech Recognition) is measured, what factors influence it, and how every improvement translates to time savings and better content workflows.
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