As voice search continues to grow, businesses need to refine their SEO strategies, especially when targeting global markets. This is where localization teams play a vital role, ensuring content is optimized for multilingual voice searches. This article explores how localization experts are leveraging technologies like schema markup, NLP, ASR, and voice analytics to help businesses capture voice search traffic across languages and regions.
1. Multilingual Schema Markup for Voice Search
Schema markup, also known as structured data, is a critical tool in voice search optimization. By helping search engines like Google and Bing understand the context of website content, schema markup can improve the chances of a website being featured in voice search results. For businesses operating in multiple regions, it’s essential to implement multilingual schema markup, allowing search engines to interpret voice queries in various languages and serve the most relevant results. Localization teams ensure that structured data reflects regional languages, local customs, and search behaviors. This involves optimizing metadata, such as product descriptions or FAQs, to cater to natural spoken language rather than traditional keyword-based searches. The key is making localized content easy for search engines to index and retrieve in voice search results, whether the query is spoken in French, Spanish, or Hindi.Read more about our dubbing and voiceover translation services
2. Technologies Enhancing Multilingual Voice Search Optimization
Localization professionals work closely with various technologies to ensure voice search queries are accurately interpreted across different languages and dialects. Below are key technologies and real-world applications:a. Natural Language Processing (NLP)
NLP allows voice assistants to interpret human speech by understanding context, intent, and the subtleties of language. Recent advancements in NLP, especially in languages beyond English, are crucial for businesses aiming to optimize for multilingual voice search. Localization teams use NLP technology to refine keyword strategies and improve the natural flow of language in queries. Case Study: Google Assistant Google has made significant strides in expanding its NLP models to support non-English languages like Hindi and Japanese. By using localized datasets to train these models, Google ensures the assistant can handle dialects, accents, and regional linguistic nuances. This expansion has allowed Google Assistant to cater to a broader audience with more conversational accuracy in multiple languages.b. Automatic Speech Recognition (ASR)
ASR is the technology that converts spoken words into text, enabling voice assistants to understand and respond to voice commands. For voice search optimization in multilingual contexts, ASR systems need to be trained on extensive datasets that reflect local speech patterns, dialects, and informal speech. Case Study: Microsoft Azure & Volkswagen Microsoft Azure’s ASR tools, used by companies like Volkswagen, are prime examples of how ASR technology can be adapted for multilingual voice search. Volkswagen uses Azure’s ASR to enable voice search for navigation and other services in cars, allowing users to give commands in their native language. This localization ensures that ASR systems can handle regional accents, making the experience seamless for users in different markets.c. Voice User Interface (VUI) Design
Voice User Interface (VUI) design is another emerging field that integrates localization. VUI design involves optimizing how users interact with voice-based platforms. When localized, the VUI must align with regional language use, including tone, formality levels, and conversational flow. Case Study: Duolingo Duolingo’s app provides a perfect example of localized VUI. As Duolingo integrated voice commands into its language-learning platform, the company worked with localization experts to adapt voice interactions to match the speech patterns and linguistic expectations of each target market. This helped ensure a natural learning experience in every language supported by the app.d. Multilingual Voice Analytics
Voice analytics platforms are increasingly used to monitor and analyze how users interact with voice search features across different languages. Localization teams can use this data to optimize content strategies and voice queries in specific regions. Case Study: Spotify Spotify introduced voice-activated playlists in multiple languages, relying on voice analytics to refine their algorithms. By analyzing requests made in French, Spanish, and Italian, Spotify was able to make localized improvements, tailoring its voice search features to suit the tastes and speaking habits of users in those regions.e. Contextual Search and AI
Contextual search, powered by AI, is critical in voice search optimization. AI interprets not just the words in a query but also the context in which they’re spoken. For example, a voice search for “best restaurants” in a particular city will vary based on local customs and food preferences. Localization teams help AI models grasp these cultural and regional nuances. Case Study: Amazon Alexa in India When Amazon expanded Alexa to India, the company localized its voice search capabilities by integrating local festivals, contexts, and slang into its responses. This allowed Alexa to provide answers that were not only linguistically accurate but also culturally relevant, significantly improving user engagement in the region.3. Real-Life Case Studies in Voice Search Localization
- Domino’s Pizza – Voice-Activated Ordering: Domino’s integrated voice search into its ordering system, allowing customers to place orders via smart assistants like Alexa and Google Assistant. In India, Domino’s localized this feature to handle voice commands in Hindi and other dialects, enhancing the customer experience.
- BMW – In-Car Voice Search: BMW uses Cerence’s AI-powered voice technology to provide in-car voice assistants in multiple languages. Localization teams adapted the system to handle regional accents, allowing drivers to interact with the voice assistant naturally in different countries.
- Alibaba – AI and Voice Search in E-Commerce: Alibaba introduced AI-powered voice search in Mandarin and expanded it to Japanese and Thai markets. Localization teams ensured that the voice search function recognized idiomatic expressions and regional shopping behaviors in each language.
- Mastercard – Multilingual Voice Payments: Mastercard’s voice payment service is now available in multiple countries. Localization efforts focused on making the system compatible with French, Spanish, and German, ensuring voice recognition handled regional accents for smooth transactions.
4. Best Practices for Localization Teams in Voice Search
- Train ASR Systems with Local Speech Patterns: Collaborate with ASR developers to ensure systems are trained on local accents, colloquial speech, and informal language.
- Optimize for Question-Based Queries: Since voice searches are often question-based, localized content should focus on answering common regional queries.
- Ensure Multilingual VUI Usability: Work closely with VUI designers to ensure user interactions are smooth and culturally appropriate in different languages.
- Leverage Voice Analytics for Continuous Improvement: Use data from voice analytics to refine voice search strategies and improve the accuracy of voice assistants across multiple languages.