1. Understanding Googles BERT and MUM Algorithms
To understand how voice search results are shaped today, its important to first get a grasp of two powerful algorithms developed by Google: BERT and MUM. These technologies play a major role in how Google understands language and delivers more accurate search results, especially for voice queries.
What is BERT?
BERT stands for Bidirectional Encoder Representations from Transformers. Released by Google in 2019, BERT helps the search engine better understand the context of words in a sentence. Instead of looking at words one-by-one or left-to-right, BERT analyzes the entire sentence bidirectionally. This means it can grasp the full meaning behind a query, even if its phrased in a conversational or natural way—just like how people talk when using voice search.
Key Features of BERT:
- Understands context and nuance
- Improves understanding of natural language
- Especially helpful for long-tail keywords and complex queries
What is MUM?
MUM stands for Multitask Unified Model. It’s a more recent algorithm introduced by Google in 2021. While BERT was a big step forward, MUM takes things even further by not only understanding language but also generating it. Plus, MUM is trained across 75+ languages and can pull information from text, images, and other formats to provide more complete answers.
Key Features of MUM:
- Multilingual understanding
- Processes multiple types of content (text, images)
- Can generate responses and insights
BERT vs. MUM: What’s the Difference?
Here’s a quick comparison to highlight how these two algorithms differ and complement each other:
| Feature | BERT | MUM |
|---|---|---|
| Year Introduced | 2019 | 2021 |
| Main Function | Understands language context | Understands & generates responses |
| Languages Supported | Mainly English at launch | 75+ languages |
| Content Types Processed | Text only | Text, images (and more) |
The Role in Voice Search
Voice searches tend to be longer and more conversational than typed queries—for example, “Where can I buy affordable running shoes near me?” instead of just “running shoes.” Both BERT and MUM help interpret these natural-language questions more accurately. BERT ensures that Google understands the full meaning behind the spoken words, while MUM can go a step further by pulling together information from different sources to answer complex questions effectively.
2. The Rise of Voice Search in the U.S.
Voice search is quickly becoming a part of everyday life for many Americans. With the popularity of smart speakers like Amazon Echo and Google Nest, more people are using their voices instead of typing to find information online. This shift is changing how search engines deliver results—and that’s where Google’s BERT and MUM algorithms come into play.
Why Voice Search Is Growing
Americans are embracing voice search because it’s fast, hands-free, and feels more natural. Whether they’re cooking dinner, driving, or multitasking at work, people appreciate being able to ask questions out loud and get quick answers. According to recent surveys, over 50% of U.S. adults use voice search daily.
Common Reasons Americans Use Voice Search
| Use Case | Example |
|---|---|
| Finding Quick Facts | “Hey Google, what time does Target close?” |
| Navigating While Driving | “Give me directions to the nearest gas station.” |
| Setting Reminders or Alarms | “Remind me to call mom at 6 PM.” |
| Shopping Online | “Order more dog food from Amazon.” |
How BERT and MUM Improve Voice Search Results
Voice searches are often conversational and more complex than typed queries. For example, someone might say, “What should I wear for a wedding in Chicago this weekend?” Instead of picking out just keywords like “wedding” or “Chicago,” Google uses BERT and MUM to understand the full meaning behind the question.
BERT (Bidirectional Encoder Representations from Transformers) helps Google understand context by looking at words before and after each term. This makes it better at interpreting natural speech patterns.
MUM (Multitask Unified Model) goes even further by analyzing language across different formats—like text, images, or even videos—and can provide deeper insights from a wider range of sources. This is especially useful for complex voice queries that require more than a simple answer.
Example: Typed vs. Voice Query
| Type of Query | User Input | Googles Response with BERT & MUM |
|---|---|---|
| Typed | “weather Chicago” | Shows temperature and forecast for Chicago. |
| Voice | “Will it rain in Chicago this weekend for a wedding?” | Takes into account date, location, event type; offers tailored weather advice. |
What This Means for SEO in the U.S.
If your content isn’t optimized for voice search yet, now’s the time to start. Since Americans often phrase voice searches as full questions or natural sentences, your content should answer those questions clearly and directly. Think about how people speak—not just how they type—and include conversational keywords throughout your site.
Tips for Voice Search Optimization:
- Use long-tail keywords that sound natural when spoken.
- Create FAQ pages that answer common voice queries directly.
- Add structured data so Google can better understand your content.
- Focus on mobile-friendly design since many voice searches happen on phones.
The rise of voice search is reshaping SEO strategies across the U.S., and understanding how BERT and MUM interpret spoken queries will give your content a competitive edge in today’s evolving digital landscape.
3. How BERT Improves Voice Search Accuracy
When we talk to voice assistants like Google Assistant, we usually speak in full sentences or questions—just like we would with another person. That’s where Google’s BERT algorithm comes in. BERT, which stands for Bidirectional Encoder Representations from Transformers, helps Google better understand the context and intent behind our words, especially in voice-based searches.
Understanding Natural Language Better
BERT is designed to interpret language more like a human does. Instead of looking at each word one by one, BERT looks at the entire sentence at once. This helps it figure out what you really mean—even when your question is vague or complex.
Example:
| Users Voice Query | Without BERT | With BERT |
|---|---|---|
| “Can you tell me who she is?” (after talking about Taylor Swift) | Might return results about random women | Understands “she” refers to Taylor Swift |
| “What time is the game on if Im in New York?” | Might ignore location context | Takes into account users location and time zone |
| “Is it okay to eat sushi while pregnant?” | Might focus just on sushi recipes | Understands health-related concern during pregnancy |
BERT Understands Context and Intent
This is especially useful for voice search because people often ask follow-up questions or use pronouns like “it,” “she,” or “that.” BERT helps Google connect those dots based on earlier parts of the conversation or search history. So instead of treating every voice query like its brand new, BERT helps Google remember what youre talking about.
Why This Matters for SEO and Marketers:
- Create content that answers real questions: Use natural language and answer-specific user intents.
- Use conversational keywords: Think about how people actually speak when using voice search.
- Add context to your pages: Include related topics, FAQs, and structured data to help algorithms like BERT understand your content better.
BERT brings a deeper understanding to how users speak and what they really want to know. For voice search, this means more accurate results—and for marketers, it means a chance to connect with users in a more meaningful way.
4. MUM’s Role in Enhancing Conversational Search
Googles Multitask Unified Model, or MUM, is a game-changer when it comes to voice search. Unlike older algorithms that rely heavily on keywords, MUM is designed to understand language more like a human. It processes information across different formats—like text, images, and even videos—and makes connections between them. This means it can handle more complex voice queries that people ask in natural, conversational ways.
What Makes MUM Different?
MUM is trained in over 75 languages and uses advanced AI to interpret the context behind your questions. So when you ask something like, “What do I need to prepare for a fall hike in the Rockies?” MUM doesnt just look for pages with matching words. It understands youre asking about weather, gear, fitness level, and possibly safety tips—all at once.
Key Features of MUM
| Feature | Impact on Voice Search |
|---|---|
| Multimodal Understanding | Can analyze and combine information from text, images, and videos to provide more complete answers. |
| Contextual Relevance | Understands the intent behind a query by looking at the broader context—not just keywords. |
| Cross-Language Training | Accesses information in multiple languages to find the best answers—even if they’re not in English. |
Real-Life Example
If someone asks their smart speaker: “Can I use hiking boots for snowshoeing?” MUM doesn’t just compare hiking boots and snowshoes—it considers expert advice from forums, product reviews, instructional videos, and blog posts. Then it delivers an answer that factors in temperature, terrain type, and user experience—things traditional search engines might miss.
Why This Matters for SEO
With MUM powering voice search results, content creators should focus on delivering comprehensive, multimodal content that answers real user questions. Think beyond keywords: include FAQs, visuals, how-to guides, and expert insights. The better your content fits into a conversation-style query, the more likely it will be surfaced by MUM during voice searches.
5. Voice Search Optimization Strategies for U.S. Brands
As Google’s BERT and MUM algorithms continue to shape how voice search results are delivered, it’s crucial for American businesses to adapt their SEO strategies. These advanced AI models focus heavily on understanding natural language and user intent, which means your content must be more conversational and context-aware than ever before.
Understand How BERT and MUM Influence Voice Search
BERT (Bidirectional Encoder Representations from Transformers) helps Google better understand the meaning behind words in a sentence, not just individual keywords. This is important for voice search, where users tend to speak in full sentences or questions.
MUM (Multitask Unified Model) goes even further by analyzing content across different formats and languages to provide more comprehensive answers. It understands context at a deeper level, which benefits long-form queries often used in voice search.
Practical SEO Tips for Voice Search Optimization
To help U.S. brands stay competitive in this evolving landscape, here are some practical tips:
Use Conversational Keywords
People use natural, everyday language when speaking to voice assistants. Instead of targeting short-tail keywords like “weather New York,” optimize for phrases like “whats the weather like in New York today?”
Create FAQ Sections
BERT favors content that directly answers questions. Adding an FAQ section using real customer queries can improve your chances of being featured in voice search results.
Focus on Local SEO
Most U.S.-based voice searches are local (e.g., “best pizza place near me”). Make sure your business listings are up-to-date, and include location-based keywords in your content.
| Local SEO Checklist for Voice Search | Status |
|---|---|
| Add your business to Google Business Profile | ☑ |
| Include city and state names in titles and meta descriptions | ☑ |
| Create content around local events or news | ☐ |
Improve Page Speed and Mobile Experience
MUM factors in user experience when ranking voice search results. Ensure your site loads quickly and is mobile-friendly since most voice searches happen on smartphones.
Add Structured Data Markup
Use schema markup to help Google understand the context of your content. This can increase the chances of being selected as a featured snippet — a common source for voice responses.
Tone and Content Style That Matches U.S. Consumers
The tone of your content should reflect how people actually talk. Use contractions (“you’re” instead of “you are”), keep sentences short, and write like you’re having a conversation with a customer.
Examples of Written vs Spoken Queries
| Typed Query | Voice Query |
|---|---|
| “best SUV 2024” | “What’s the best SUV to buy in 2024?” |
| “coffee shops NYC” | “Where can I find a good coffee shop in New York City?” |
The shift toward natural language processing through BERT and MUM means that writing for people — not just search engines — is more important than ever. By creating helpful, conversational, locally relevant content, American businesses can improve their visibility in voice search results and connect more authentically with users.
