Automatic Speech Recognition vs. Traditional Transcription

Whenever you need to convert spoken language into written text, the debate between speech recognition vs transcription has gained prominence. 

These two distinct methods, Automatic Speech Recognition (ASR) and Traditional Transcription offer unique approaches to address this task. Both serve the purpose of transforming spoken words into written text, but they do so using different approaches and technologies.

This article explores this topic further by dissecting:

• What traditional transcription is;
• What speech recognition technology is;
• The key benefits and drawbacks of each method;
• How Krisp’s AI technology makes your transcriptions seamless.

What is Traditional Transcription?

Traditional transcription entails using manual human effort to convert spoken words into written text. Basically, it involves a person listening to an audio or conversation and converting it into written words. 

This method is referred to as ‘traditional transcription’ as it doesn’t require much technology involved — just the good old human understanding. 

Although this method can sometimes be referred to as ‘archaic,’ it is still useful in various instances, including healthcare, legal, media, and research purposes. This is especially true in situations where context and nuance are critical. 

So, how exactly does traditional transcription work? 

How Does Traditional Transcription Work?

Since traditional transcription depends on the understanding of the person doing the transcription, it may vary from one person to another. However, a good transcription often requires five distinct steps or phases, including: 

1. Listening and Understanding

The traditional transcription process begins with a human transcriptionist who listens to audio recordings or live conversations. 

It is vital that the transcriptionist has stellar listening, hearing, and linguistic skills to allow them to comprehend the spoken words accurately.

2. Typing

As the transcriptionist listens to the audio, they simultaneously type out the spoken words. 

Live transcriptions, such as those used in courtrooms or for legal purposes, often require additional skills to enable the transcriptionist to capture what was said accurately, including the nuances in the conversations. 

3. Formatting

Traditional transcriptions may require the transcriptionist to format the text to boost its readability and understanding. 

Traditional transcriptionists often adhere to specific formatting guidelines, which may vary depending on the industry or client’s preferences. Although this process may take additional time, it is a vital step in ensuring the transcription is up to par with the required standards. 

4. Editing and Review

After the initial transcription is complete, the transcript undergoes a thorough review. Transcriptionists check for errors, correct typos or inaccuracies, and ensure that the transcript faithfully represents the spoken content.

5. Quality Assurance

Some transcription services include a quality assurance step to further enhance accuracy. A second transcriptionist may review the transcript independently to ensure the highest level of precision before they are shared with the relevant parties or archived for future reference. 

Benefits of Traditional Transcription

Traditional transcription has been in use for many years, and even though there is newer technology to handle this kind of work, there are some benefits that still make it useful to date.

• Accuracy

One of the most significant advantages of traditional transcription is its high level of accuracy. Human transcriptionists can capture nuances, accents, and context with a level of precision that automated systems may struggle to achieve.

• Contextual understanding

Transcriptionists can understand and convey the subject matter, identify different speakers, and capture emotions and nuances, making it ideal for fields like legal or medical transcription.

• Customization

Traditional transcriptionists can adapt to specific requirements, jargon, or formatting preferences, tailoring the transcripts to the client’s needs.

• The ability to handle complex content

Traditional transcription is well-suited for handling complex and technical content, such as medical or scientific discussions. 

Transcriptionists can research terminology and ensure accurate representation, whereas automated systems may struggle with specialized vocabulary.

• Speaker identification

Human transcriptionists can accurately identify and label speakers, making it easy for readers to follow the conversation and understand who is speaking at any given moment. 

This is especially helpful when there are numerous speakers in an audio or conversation, where labeling using automated services may be tedious.

Drawbacks of Traditional Transcription

Traditional transcription may offer numerous benefits. However, they also have disadvantages that newer technologies seek to resolve, including: 

• It is a time-consuming process

Traditional transcription is a time-intensive process. It can take several hours to transcribe just one hour of audio, making it less suitable for tasks that require quick turnaround times.

• Costly

Hiring human transcriptionists can be expensive, especially for large-scale transcription projects, which may make it less cost-effective compared to automated solutions like ASR.

• Prone to human error

Like any manual task, traditional transcription may not be perfect. It is susceptible to human errors, including typos and misinterpretations, which can impact the overall quality of the transcripts.

Furthermore, human transcription may be biased and may not capture the conversation or audio as it is. This is because the transcriptionist is human and may fail to capture the audio as it should be.

• Limited scalability

Assuming you want to have written records of all your conversations or meetings, it might take you a long period to have this achieved. This is mainly because this is limited to the number of transcriptionists at your disposal. 

This limits your scalability, as you will spend more resources and subject your records to more human error.

• Fatigue and burnout

You can easily listen to audio and convert it into text. However, if you decide to take this route, you may subject yourself to fatigue and burnout, which may not be a healthy practice. 

Most transcriptionists often face challenges such as listening to poor audio quality, heavy accents, or emotionally charged content. It even gets more difficult for them to do the right transcription when faced with issues such as virtual meeting fatigue.

• Privacy and security concerns

Hiring an in-house transcriptionist to keep accurate records of all your meetings and other important conversations can be quite expensive and exhausting. Some people often prefer to outsource their transcription needs.

However, outsourcing transcription services raises concerns about data privacy and security. Sharing sensitive or confidential information with external transcriptionists could pose risks if proper safeguards are not in place.

What is Speech Recognition?

Also known as Automatic Speech Recognition (ASR), Speech Recognition is the extraordinary ability of computers and devices to comprehend and convert spoken language into written text. 

In this case, minimal manual output is required from a person, as the computer does all the heavy lifting. 

So, how does Automatic Speech Recognition work? 

How Does Speech Recognition Work?

Different speech recognition technologies work differently. However, most advanced ASR products follow a similar pattern, although with unique specifications. Here’s how, in general, ASR systems work: 

Audio input

ASR systems require an audio input to work. This can be in the form of live speech, recorded audio, or voice commands directed at devices like smartphones, smart speakers, or computers.

This is beneficial in different types of meeting setups, including remote or hybrid virtual meetings. 

Acoustic modeling

Speech recognition systems use acoustic modeling to analyze the incoming audio signal. This step involves identifying phonetic and linguistic features like pitch, tone, and pronunciation.

Language modeling

After acoustic analysis, the system employs language modeling to predict the most likely sequence of words or commands that correspond to the given audio. This phase takes into account contextual information, grammar, and syntax to enhance accuracy.

Decoding and text generation

The final stage involves the system decoding the audio signal and generating a corresponding text output. The output can be used for various applications, including various types of transcription, voice assistants, and more.

Benefits of Speech Recognition

ASR systems offer a wide array of benefits, including:

• Improved speed and efficiency

Speech recognition systems work significantly faster than manual transcription methods. This means you can access your transcriptions in real-time or near real-time. This makes it ideal for tasks that require a faster turnaround, eventually boosting efficiency. 

• Cost-effective transcription solution

Using Speech Recognition solutions can be more cost-effective than hiring manual human transcriptionists. This is especially helpful to organizations that need to process large volumes of audio content regularly.

• Automation

Automation is a key ingredient in today’s world, where everything has to be done faster, easier, and with increased productivity in mind. This is what ASR systems do.

Speech recognition can be seamlessly integrated into various applications and meeting productivity tools, automating tasks that would otherwise require manual intervention.

• Increased productivity

Speech recognition can significantly boost productivity, especially in professional settings. It allows users to dictate text, navigate applications, and perform various tasks more efficiently than manual typing or clicking.

• Useful in transcription services

Speech recognition technology is a valuable tool for transcription services, automating the initial transcription process and allowing human transcribers to focus on refining the output, thus increasing efficiency.

Drawbacks of Speech Recognition

Although Speech Recognition has numerous benefits, it also carries with it a few challenges, including: 

• Accuracy challenges

While ASR technology has made significant advancements, it may not always achieve the same level of accuracy as human transcriptionists, particularly in cases involving heavy accents, background noise, or complex technical content.

• Lack of context

ASR systems may struggle to capture nuances, context, or emotions conveyed through speech, which humans can interpret more accurately.

• Privacy concerns

Voice data collected and processed by certain speech recognition systems can raise privacy and security concerns, particularly regarding sensitive or personal information.

Krisp: Unleashing AI Magic for Flawless Transcriptions and Speech Recognition

Krisp is a game-changer in the ever-evolving AI landscape, revolutionizing transcription and speech recognition. 

Krisp’s AI meeting assistant removes the hassle of human transcribers, errors, and time-consuming processes, saving you time, enhancing productivity, and bringing unparalleled accuracy to your meetings and conversations.

Krisp employs advanced machine learning and Natural Language Processing (NLP) to offer impeccable meeting transcriptions, eliminating the need for human transcribers prone to fatigue and inaccuracies. 

Krisp’s AI transcription service ensures that every word and every detail is accurately captured, making your meetings more efficient and your records more reliable.

Leveraging the transcriptions, Krisp’s AI note-taker generates meeting summaries that encompass meeting takeaways, discussions, decisions, and action items. 

Moreover, it is a no-brainer that background noises can be the bane of accurate transcriptions. Krisp has that covered, too. With its AI noise cancellation feature, Krisp ensures that background distractions fade into oblivion, leaving you with crystal-clear transcriptions. Every word spoken is precisely transcribed, regardless of the surrounding environment.

 

Try Krisp for free today and witness firsthand the accuracy, efficiency, and productivity this AI system brings to your conversations and records. 

 

Get Krisp for Free

 

Frequently Asked Questions

Which is more accurate: speech recognition or traditional transcription?

Accuracy can vary depending on the specific context and audio quality. However, in general, Speech Recognition technologies like Krisp offer a more accurate reflection of the actual conversation in the transcription.

Is speech recognition faster than traditional transcription?

Yes, speech recognition is typically much faster than traditional transcription. ASR systems can transcribe audio in real-time or near real-time, while human transcriptionists require significantly more time to transcribe the same content. 

Can speech recognition handle different accents and languages?

Most modern speech recognition systems are designed to handle various accents and languages. However, some ASR models may require customization or fine-tuning for specific accents or languages to achieve optimal accuracy.