Speech recognition may rank among the most fascinating developments in history. Who’d ever thought that machines would come to learn our words? Although the technology is far from understanding our intent, it’s improving rapidly. Understandably, some industries, like law enforcement, are looking into implementing speech recognition in their daily process. And indeed, speech recognition may be great for many tasks. However, law enforcement transcription work requires the nuance and critical precision that only transcription companies for law enforcement reliably display.
So, what are the proper applications for speech recognition in law enforcement? Can they be used for transcription at all?
In this article, you’ll learn how:
- Speech recognition is effectively used for real-time language translation during field interviews, though it serves as a temporary solution until human translators arrive.
- Automated dispatcher assistance can now flag concerning phrases and detect background noises that might indicate emergencies, though not with perfect accuracy.
- Due to the complexity of human speech patterns, current AI transcription technology achieves only about 61.92% accuracy, falling short of the 99% accuracy needed for law enforcement documentation.
What Is Speech Recognition and How Does It Work?
Speech recognition works through a database of digital signal processing and accounting modeling that turns sound waves into commands the machines recognize.
Think of Alexa and Google Home. The speech recognition process for these products starts with capturing sound waves through a microphone. Then, these sound waves are digitalized through analog-to-digital conversion. The system then performs spectral analysis using Fourier Transform to break down waveforms into their frequency components to identify phonemes—the building blocks of speech. The spectral features are then analyzed using Hidden Markov Models (HMMS) and neural networks trained on human speech pattern datasets.
Simply put, when you tell Alexa to set the alarm for 6 am, the system processes your command through multiple layers of processing and then applies language models to get the most probable word sequence. (Take note of the emphasis here; we’ll discuss it further later.)
Common Types of Speech Recognition
There are multiple speech recognition types, each with a specific intended use.
Type | Description |
Speaker-Dependent | Designed for single-user recognition, requires individual voice training for high accuracy. |
Speaker-Independent | Works with multiple users without training, used in customer service systems and public interfaces. |
Continuous Speech | Processes natural, flowing speech without the need for pauses between words. Powers modern virtual assistants and transcription services. |
Discrete Speech | Requires distinct pauses between words, useful in environments with high background noise or for specific command systems. |
Natural Language Processing | Understands context and intent beyond simple word recognition, enabling conversational interactions. |
Command and Control | Specialized for specific predefined commands, ideal for device control and simple navigation systems. |
Interactive Voice Response | Designed for automated phone systems, handles basic queries and menu navigation through voice input. |
Speech-to-Text | Focuses on converting spoken words to written text, commonly used in dictation and transcription applications. |
How Can Speech Recognition Be Used In Law Enforcement?
Law enforcement agencies can be a bit resistant to change, as they tend to stick to the old and proven ways of doing things. Still, the use of speech recognition for law enforcement operations is getting some serious consideration. Here are its potential uses:
Real-time Language Translation During Field Interviews
For real-time translation, nothing beats speech recognition technology. These services and platforms can be easily accessed from a police officer’s phone, and can immediately facilitate communications, at least until a human translator is deemed necessary.
Of course, the trade-off for immediate, convenient translation is accuracy. Like with most things driven by automated processes, speech recognition does not account for tone, word choice, idioms, or nuance—things that human translators with effective command of their language wouldn’t miss.
Voice-activated Controls in Patrol Vehicles
Although it may sound a bit too futuristic, voice-activated cars are hitting the roads just like those in movies. Of course, police cars won’t get left behind in these innovations. Speech recognition systems can then be integrated into the vehicle’s computer for a seamless voice-controlled environment.
Let’s say the officer spots a reckless driver while checking their computer. In that case, they can activate their lights and siren, call dispatch, and log the incident start time—all without taking their hands off the wheel. It may not have been possible five years ago. Still, technology has developed to understand commands even in high-stress situations where an officer’s voice might be speaking very rapidly for some reason.
Voice Recognition for Suspect Identification
With the latest voice recognition, our voices have become much like our fingerprints—except we can’t wear gloves to hide them. Now, some of you might want to remind me that voice changers exist to beat such measures.
Voice recognition systems can now help identify individuals even when they disguise their voice with a voice-changer or speak a different language. Take note: speech recognition tech isn’t simply matching voice patterns; it analyzes hundreds of unique vocal characteristics that are nearly impossible to fake consistently.
Suspects who call in threats will be easily detected as the system can compare their voices against known offenders while the call remains active. They create a voiceprint that considers speech factors like accent, speaking patterns, vocal tract length, breathing patterns, etc.—everything that can be particularly valuable in solving cases that involve phone scams or ransom calls.
Automated Dispatcher Assistance
Some dispatch systems now use speech recognition to monitor calls, which helps by flagging phrases or emotional spike indicators that might signal an escalating situation.
It works by creating real-time transcripts that highlight potential concerns that dispatchers might miss due to heated situations. For example, if the caller mentions his location multiple times in different ways, the system can automatically cross-reference these details to pinpoint the most plausible address.
Also, the AI can detect background noises that might signal specific types of emergencies—background noises like breaking glass, vehicle engines, dogs barking widely, babies crying intensely, you name it. Although not with 100% accuracy, AI can help put the pieces together and solve the bigger puzzle.
Can Speech Recognition Be Used In Law Enforcement Transcription?
You’ll notice that transcription is conspicuously absent from all the benefits of speech transcription for law enforcement I’ve mentioned. There’s a good reason for that.
Recording and transcription have classically been done by people who learned the trade through years of practice and improvements. With its impressive learning capability and logical processes, AI should be able to replicate a human’s training quickly, at least in theory.
Yet, these programs still have issues transcribing to near-perfect accuracy. Latest tests show that AI can only transcribe audio files with 61.92% accuracy. Why is that?
The answer is simple: human speech is difficult to understand.
Think back on the last time you talked to someone. Your conversation included verbal and nonverbal speech. Aside from that, there may have been hidden nuances in your talks—things that are not stated outright yet are communicated nonetheless.
Now, consider how you understood the levels of the conversation. Did you think through it logically, or did it… happen automatically?
Human speech is incredibly nuanced, and it takes us a long time to understand it instinctively.
Unfortunately, AI does not have an instinct. Therefore, it is at the mercy of the concepts we take for granted, like figures of speech, nuance, and contextual understanding. It only spits out what it thinks it heard, while human transcribers can apply their knowledge and experience to transcribe the passage correctly.
So far, we haven’t found a way to train AI to perfectly recognize and transcribe human speech, not like the way we do. That’s why AI transcription suffers from inaccuracy—and that’s why human transcription is still the golden standard.
Let’s Talk About Your Law Enforcement Transcription Needs
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We guarantee 99%+ accuracy for all law enforcement transcription jobs, something that speech recognition can’t even touch. Aside from that, we offer fast turnaround times, transparent and tiered rates for different needs and budgets, unparalleled customer service, and CJIS-compliant security features. Rest easy knowing your transcripts will always arrive to you as safe and as accurate as they can be.
If you want to be part of our success story, take us for a test drive with our free, no-commitment. Trust me; you’ll want no one else to do law enforcement transcription when you experience the Ditto difference.
Ditto Transcripts is a CJIS-compliant, Denver, Colorado-based transcription company that provides fast, accurate, and affordable transcription services to companies and agencies of all sizes. Call (720) 287-3710 today for a free quote, and ask about our free five-day trial. Visit our website for more information about our transcription services.