October 30, 2024
How Audio Analysis Can Detect Robocalls More Efficiently
As robocalls continue to infuriate consumers in 2024, a new tool might help solve this problem for good. Audio analysis allows honeypots, service providers, and regulators like the FTC to identify patterns in call behavior, analyze the context of recordings, and work out whether they are legit. You can learn more about this technology below.
How Do Robocallers Detect AMD?
Robocallers already use a type of audio analysis to carry out their schemes. Answering machine detection (AMD) analyzes calls and determines whether a voicemail or human answered the line. When a real person answers a call, they often use a short response, such as “hello?” However, voicemail greetings are typically longer.
Most illegal robocallers don’t want to leave voicemails, and AMD helps them achieve this goal. Voicemails allow service providers and regulators to analyze robocallers, making it easier to track their origin.
Some voice recording systems, including those associated with honeypots, get around AMD by offering preset greetings that mimic human responses. For instance, a greeting might include “hello” followed by a pause. This can trick AMD into thinking a human has answered a call, allowing robocalling systems to play pre-recorded messages that telecom companies and authorities can track.
Recording and Analysing Robocall Data
By tricking AMD and capturing recorded data from robocallers, parties can analyze those communications and determine the intent of calls. Service providers, for example, might evaluate the following from recorded audio when evaluating a robocall:
- A robocaller might say the name of the business they are calling from in pre-recorded audio, helping service providers work out whether a company is legitimate or not.
- A robocaller might reveal their name in a message, which can be useful for determining the legitimacy of a call.
- Robocallers often leave other details in a voicemail, such as the reason they are calling.
Service providers can also analyze the calling party telephone number associated with a robocall, which is the number displayed to the call recipient on their phone. The name of the robocaller and call-back telephone number might also be available for analysis.
Identifying Bad Actors with Audio Analysis
Audio analysis helps identify whether robocalls are legal and genuine or pose a potential threat to consumers. By evaluating audio recordings, voicemail content, and other information, telecom companies and regulators can reduce the number of illegal robocalls and improve calling experiences for the public.
For example, audio analysis can determine whether a robocall entity is an established and legitimate company or a bad actor by evaluating patterns in call language. Robocallers often use similar phrases that create emotional reactions in the people they are talking to, such as “We can help with a refund,” “Your electricity will be shut off by the end of the day,” and “We need you to download this to your phone.”
Service providers and regulators can also determine whether a robocall is illegal before audio analysis by identifying and validating phone numbers. Often, robocallers call from numbers that have been previously associated with fraudulent campaigns, suggesting pre-recorded messages originate from bad actors.
Sometimes, analysis might reveal that a bad actor spoofed a phone number belonging to a real business. This is a common tactic used by illegal robocallers, which can make it hard for consumers to know the legitimacy of a call.
Solutions to Analyze Audio Data
Analyzing pre-recorded messages is a mammoth task that involves evaluating large sets of data. However, analysis is essential to identify robocallers and stop them from doing further damage. Here are some ways to analyze audio data:
Natural Language Processing (NLP)
NLP analyzes the content of an audio message to figure out whether it came from a robocaller. It understands and interprets human language and looks for patterns by combining computer science, artificial intelligence, and other technologies. NLP helps service providers block illegal robocalls and prevent scams.
Voice Print Analysis (VPA)
VPA reproduces human sound waves on a sound graph via electrical impulses, allowing service providers and other parties to compare two recorded voice samples—one of a known person and one of an unidentified person. That can help them decide whether a robocall is genuine.
Spectrogram Analysis (SA)
A spectrogram visually represents a call signal’s strength or loudness at different frequencies in a specific waveform. This can be helpful in identifying pre-recorded audio files that are part of robocall campaigns.
Digital Fingerprinting
A digital or audio fingerprint is a summary of an audio signal that can identify a pre-recorded message or locate similar messages in an audio database. The FTC maintains a database of audio fingerprints of robocalls through anti-robocall devices, anti-robocall apps, online consumer uploads of files, and other sources.
The Future of Audio Analysis for Robocalls
Analyzing pre-recorded audio left by robocallers benefits the public and reduces fraud. This technology is still in its infancy and will continue to evolve, making it even easier to identify illegal communications and protect consumers, restoring confidence in your business. When used alongside other measures, such as the Do Not Call Registry and stricter regulatory enforcement, audio analysis might be a powerful tool for preventing robocalls.
