Though Google formally supports a limited number of languages in NotebookLM, the AI tool’s potential is far broader and deeper. Language stands as one of the most promising frontiers for creative and functional developments in NotebookLM’s use cases.
NotebookLM’s Linguistic Capabilities
At its core, NotebookLM primarily relies on written text to analyse data, summarise insights, and generate responses. However, the AI’s abilities extend beyond text: it can transcribe audio into text and then process this transcribed data with its usual analytical capabilities. This dual nature—working with written and spoken inputs—makes it essential to attribute two distinct sets of linguistic capabilities to NotebookLM:
Audio Support: A shorter list of languages that can be broadly synthesised and transcribed into text. Here, speech recognition adapts well to commonly spoken languages and regional accents as long as speech is clear and enunciated. when transcribed, these can be classified according to their dialect
Written Text Support: A broader list that accommodates variations in spelling, grammar, and vocabulary specific to regions, dialects, and writing systems. Written inputs, unlike spoken ones, require precise classification of languages, scripts, and regional variants to ensure accurate analysis.
Languages Supported by NotebookLM: A Focus on Audio
The list below is restricted to audio capabilities because spoken language processing, while impressive, operates differently from written text. All these languages can be written—and some, like Konkani that traditionally had no script, can even be expressed in multiple scripts (Devanagari, Roman, Kannada, and Malayalam). However, NotebookLM’s ability to process written languages is far greater and more versatile. For example, you can write: –
Hindi in the Malayalam script, and NotebookLM will still interpret it accurately.
Urdu in the Roman script, which is commonly used in informal digital communication.
Arabic dialects in Latinised phonetic forms (commonly used for texting).
This remarkable flexibility opens up a whole new realm of possibilities for AI-assisted multilingual writing, including script conversions, transliterations, and cross-script understanding. Such capabilities deserve their own detailed exploration, as they far exceed the scope of audio processing alone.
For now, the discussion here is limited to NotebookLM’s audio capabilities for accepting content as an audio source. This refers to its ability to understand and transcribe spoken languages into text. The following list highlights languages where clear speech and enunciation enable the AI to process and analyse audio effectively. Languages marked as endorsed by Google have mature support, while the remainder, classified as user-verifiable, may occasionally encounter glitches.
| No. | Language | Script/Name in Original Script | Verification Status |
|---|---|---|---|
| 1 | Afrikaans | ✅ Google Endorsed | |
| 2 | Albanian | Shqip | ✅ Google Endorsed |
| 3 | Amharic | አማርኛ | ✅ Google Endorsed |
| 4 | Arabic | العربية | ✅ Google Endorsed |
| 5 | Armenian | Հայերեն | ✅ Google Endorsed |
| 6 | Assamese | অসমীয়া | ✅ Google Endorsed |
| 7 | Azerbaijani | Azərbaycan dili | ✅ Google Endorsed |
| 8 | Bashkir | Башҡورت теле | ✔ User Verified |
| 9 | Basque | Euskara | ✅ Google Endorsed |
| 10 | Bhojpuri | भोजपुरी | ✔ User Verified |
| 11 | Bodo | बड़ो | ✅ Google Endorsed |
| 12 | Bosnian | Bosanski | ✅ Google Endorsed |
| 13 | Bulgarian | Български | ✅ Google Endorsed |
| 14 | Burmese | မြန်မာဘာသာ | ✅ Google Endorsed |
| 15 | Cantonese | 廣東話 | ✔ User Verified |
| 16 | Cantonese (HK) | 香港粵語 | ✔ User Verified |
| 17 | Catalan | Català | ✅ Google Endorsed |
| 18 | Chinese | 中文 | ✅ Google Endorsed |
| 19 | Coptic | Ⲛⲟⲩⲛⲟⲩ | ✔ User Verified |
| 20 | Corsican | Corsu | ✔ User Verified |
| 21 | Croatian | Hrvatski | ✅ Google Endorsed |
| 22 | Czech | Čeština | ✅ Google Endorsed |
| 23 | Danish | Dansk | ✅ Google Endorsed |
| 24 | Dogri | डोगरी | ✅ Google Endorsed |
| 25 | Dutch | Nederlands | ✅ Google Endorsed |
| 26 | Dzongkha | རྫོང་ཁ | ✅ Google Endorsed |
| 27 | English | English | ✅ Google Endorsed |
| 28 | Esperanto | ✔ User Verified | |
| 29 | Estonian | Eesti keel | ✅ Google Endorsed |
| 30 | Faroese | Føroyskt | ✔ User Verified |
| 31 | Filipino | Filipino | ✅ Google Endorsed |
| 32 | Finnish | Suomi | ✅ Google Endorsed |
| 33 | French | Français | ✅ Google Endorsed |
| 34 | Galician | Galego | ✅ Google Endorsed |
| 35 | Georgian | ქართული | ✅ Google Endorsed |
| 36 | German | Deutsch | ✅ Google Endorsed |
| 37 | Greek | Ελληνικά | ✅ Google Endorsed |
| 38 | Gujarati | ગુજરાતી | ✅ Google Endorsed |
| 39 | Hausa | هَوُسَ | ✅ Google Endorsed |
| 40 | Hebrew | עברית | ✅ Google Endorsed |
| 41 | Hindi | हिन्दी | ✅ Google Endorsed |
| 42 | Korean | 한국어 | ✅ Google Endorsed |
| 43 | Zulu | isiZulu | ✅ Google Endorsed |
| 44 | Hawaiian | ʻŌlelo Hawaiʻi | ✔ User Verified |
| 45 | Hebrew | עברית | ✅ Google Endorsed |
| 46 | Hinglish | ✔ User Verified | |
| 47 | Hungarian | Magyar | ✅ Google Endorsed |
| 48 | Icelandic | Íslenska | ✅ Google Endorsed |
| 49 | Igbo | Asụsụ Igbo | ✅ Google Endorsed |
| 50 | Indonesian | Bahasa Indonesia | ✅ Google Endorsed |
| 51 | Interlingua | ✔ User Verified | |
| 52 | Irish | Gaeilge | ✅ Google Endorsed |
| 53 | Italian | Italiano | ✅ Google Endorsed |
| 54 | Japanese | 日本語 | ✅ Google Endorsed |
| 55 | Janglish | (Japan) | ✔ User Verified |
| 56 | Javanese | Basa Jawa | ✔ User Verified |
| 57 | Kannada | ಕನ್ನಡ | ✅ Google Endorsed |
| 58 | Kazakh | Қазақ тілі | ✅ Google Endorsed |
| 59 | Khmer | ខ្មែរ | ✅ Google Endorsed |
| 60 | Kinyarwanda | Ikinyarwanda | ✅ Google Endorsed |
| 61 | Klingon | tlhIngan Hol | ✔ User Verified |
| 62 | Konkani | कोंकणी / ಕೊಂಕಣಿ / കൊങ്കണി / كُنْكٗنِى | ✅ Google Endorsed |
| 63 | Kurdish | Kurdî | ✔ User Verified |
| 64 | Kyrgyz | Кыргызча | ✅ Google Endorsed |
| 65 | Lao | ລາວ | ✅ Google Endorsed |
| 66 | Latin | Latina | ✔ User Verified |
| 67 | Latvian | Latviešu | ✅ Google Endorsed |
| 68 | Lithuanian | Lietuvių | ✅ Google Endorsed |
| 69 | Luxembourgish | Lëtzebuergesch | ✅ Google Endorsed |
| 70 | Macedonian | Македонски | ✅ Google Endorsed |
| 71 | Maithili | मैथिली | ✔ User Verified |
| 72 | Malay | Bahasa Melayu | ✅ Google Endorsed |
| 73 | Malayalam | മലയാളം | ✅ Google Endorsed |
| 74 | Maltese | Malti | ✅ Google Endorsed |
| 75 | Manglish | (Malaysia, Singapore) | ✔ User Verified |
| 76 | Maori | Te Reo Māori | ✔ User Verified |
| 77 | Marathi | मराठी | ✅ Google Endorsed |
| 78 | Mongolian | Монгол хэл | ✅ Google Endorsed |
| 79 | Nepali | नेपाली | ✅ Google Endorsed |
| 80 | Norwegian | Norsk | ✅ Google Endorsed |
| 81 | Odia | ଓଡ଼ିଆ | ✅ Google Endorsed |
| 82 | Pashto | پښتو | ✅ Google Endorsed |
| 83 | Persian | فارسی | ✅ Google Endorsed |
| 84 | Polish | Polski | ✅ Google Endorsed |
| 85 | Portuguese | Português | ✅ Google Endorsed |
| 86 | Punjabi | ਪੰਜਾਬੀ | ✅ Google Endorsed |
| 87 | Romanian | Română | ✅ Google Endorsed |
| 88 | Romansh | Rumantsch | ✔ User Verified |
| 89 | Russian | Русский | ✅ Google Endorsed |
| 90 | Samoan | Gagana Samoa | ✔ User Verified |
| 91 | Sanskrit | संस्कृतम् | ✔ User Verified |
| 92 | Scottish Gaelic | Gàidhlig | ✔ User Verified |
| 93 | Serbian | Српски | ✅ Google Endorsed |
| 94 | Sicilian | Sicilianu | ✔ User Verified |
| 95 | Sindhi | سنڌي | ✅ Google Endorsed |
| 96 | Singlish | (Singapore) | ✔ User Verified |
| 97 | Sinhala | සිංහල | ✅ Google Endorsed |
| 98 | Slovak | Slovenčina | ✅ Google Endorsed |
| 99 | Slovenian | Slovenščina | ✅ Google Endorsed |
| 100 | Somali | Af-Soomaali | ✅ Google Endorsed |
| 101 | Spanish | Español | ✅ Google Endorsed |
| 102 | Sundanese | Basa Sunda | ✔ User Verified |
| 103 | Swahili | Kiswahili | ✅ Google Endorsed |
| 104 | Swedish | Svenska | ✅ Google Endorsed |
| 105 | Tagalog | Tagalog | ✔ User Verified |
| 106 | Tajik | Тоҷикӣ | ✔ User Verified |
| 107 | Tamil | தமிழ் | ✅ Google Endorsed |
| 108 | Tatar | Татар теле | ✔ User Verified |
| 109 | Telugu | తెలుగు | ✅ Google Endorsed |
| 110 | Thai | ภาษาไทย | ✅ Google Endorsed |
| 111 | Tibetan | བོད་ཡིག | ✔ User Verified |
| 112 | Tok Pisin | Tok Pisin | ✔ User Verified |
| 113 | Turkish | Türkçe | ✅ Google Endorsed |
| 114 | Turkmen | Türkmençe | ✔ User Verified |
| 115 | Ukrainian | Українська | ✅ Google Endorsed |
| 116 | Urdu | اردو | ✅ Google Endorsed |
| 117 | Uyghur | ئۇيغۇرچە | ✔ User Verified |
| 118 | Uzbek | O‘zbekcha | ✅ Google Endorsed |
| 119 | Vietnamese | Tiếng Việt | ✅ Google Endorsed |
| 120 | Welsh | Cymraeg | ✅ Google Endorsed |
| 121 | Xhosa | isiXhosa | ✅ Google Endorsed |
| 122 | Yiddish | ייִדיש | ✔ User Verified |
| 123 | Yoruba | Èdè Yorùbá | ✅ Google Endorsed |
| 124 | Zulu | isiZulu | ✅ Google Endorsed |
The Dynamic Role of AI in Language Adaptation
NotebookLM represents the next step in AI linguistics, where multilingual support is not just about recognition but seamless understanding and interaction. This advancement has transformative implications for lesser-known and underrepresented languages:
Spoken Language Recognition: Languages with smaller speaker bases—such as Cree, Tok Pisin, and Inuktitut—can already be processed using AI tools, provided speakers articulate clearly. This clarity helps overcome current limitations in training datasets, which remain sparse for these languages.
Dialect and Regional Variations: AI tools, including NotebookLM, are advancing their ability to recognise regional variations of global languages like English, German, Arabic, and French. For instance: –
English: Whether spoken in the UK, the US, Singapore, or India, AI recognises and synthesises these dialects into standard written forms (EN-US).
Malay: Variants like Bahasa Malaysia and Bahasa Melayu Singapura share core linguistic features while adapting to cultural contexts.
Arabic: Modern Standard Arabic (MSA) coexists with regional dialects like Gulf Arabic or Levantine Arabic, which AI tools are learning to process effectively.
Constructed and Cultural Languages: Languages like Klingon (fictional) or Esperanto (auxiliary) demonstrate the creative possibilities for AI linguistics. While niche, these languages are valuable for enthusiasts, creators, and educators. NotebookLM and similar tools can already process text-based inputs in these languages.
Rationale for Grouping Languages like English, German, and French
Mutual Intelligibility in Speech: Despite significant regional variations in vocabulary, pronunciation, and even grammar, these languages maintain a high degree of mutual intelligibility when spoken clearly and enunciated properly.
AI Adaptability: Google’s AI and similar speech recognition tools are designed to adapt to various dialectical inputs, provided the speech is clear.
Avoiding Overrepresentation: Including every regional variant as a separate entry risks unnecessary redundancy.
Broad Classifications of Languages in the List
To describe the diversity of languages comprehensively, the following broad classifications have been identified:
| Category | Examples |
|---|---|
| Natural Languages | English, French, Hindi, Arabic |
| Creole Languages | Haitian Creole, Tok Pisin, Nigerian Pidgin, Singlish |
| Constructed Languages | Esperanto, Toki Pona, Klingon |
| Extinct or Revived Languages | Latin, Akkadian, Coptic |
| Endangered Languages | Breton, Sherdukpen, Inuktitut |
| Code-switching hybrids | Hinglish |
| Regional Variants/Dialects | Cantonese, Malay (Singapore), Hakka Chinese |
The Expanding Use Cases of AI-Enabled Languages
NotebookLM’s ability to work with both spoken and written language unlocks numerous use cases across industries, communities, and creative fields: – Preservation of Endangered Languages: AI tools can document and analyse languages with few remaining speakers, such as Breton, Sherdukpen, or Inuktitut.
Multilingual Content Creation: For content creators, NotebookLM can generate summaries, insights, and translations in multiple languages.
Cross-Cultural Education: Educational materials can be transcribed, summarised, and translated into multiple languages, providing inclusive learning experiences.
Business and Customer Interaction: AI-powered tools are becoming critical for businesses aiming to cater to multilingual customers.
Preparing for the Future: Clarity, Enunciation, and Ethical Training
As AI linguistics evolves, speakers of underrepresented languages must take proactive measures to benefit from tools like NotebookLM:
Clear Speech: For spoken language transcription, clear enunciation remains critical.
Community-Driven Data Collection: Linguistic communities can collaborate to build high-quality datasets for their languages.
Privacy-Conscious Development: Linguistic data must be collected anonymously and securely.
The Future of AI Linguistics: Unlocking the Power of Every Language
The field of AI linguistics is rapidly becoming one of the most vibrant and transformative domains within artificial intelligence. While global languages like English, Mandarin, and Spanish dominate AI development today, there is an undeniable shift toward embracing linguistic diversity, with initiatives targeting regional, minority, and even artificial languages.
Pioneering Efforts: Beyond the Mainstream
AI initiatives like Speech Lab’s Singlish AI, alongside Google’s multilingual speech recognition systems, are early indicators of this movement. These tools not only adapt to widely spoken regional variants but also serve as a proof-of-concept for the integration of lesser-known languages.
The Nascent Stage: Challenges and Opportunities
Despite promising developments, AI linguistics remains in its nascent stage, particularly for underrepresented and niche languages. Several challenges persist, including: –
Data Scarcity: AI models require large, high-quality datasets for training. – Pronunciation and Enunciation: Non-major language speakers often need to speak with exceptional clarity. – Privacy Concerns: Linguistic data collection raises ethical questions surrounding privacy. However, these challenges are also opportunities. By focusing on inclusive AI development, researchers can address these gaps and harness the power of diverse linguistic inputs.
AI as a Catalyst for Linguistic Preservation
The potential of AI linguistics extends beyond functional communication. For many languages at risk of extinction, AI represents a lifeline for preservation and revitalisation.
A Future of Linguistic Equity
Looking ahead, we stand at the threshold of a transformative era in which even the world’s least commonly spoken languages can be integrated into AI systems. The potential rests in the capacity of AI to adapt and learn from small datasets, thereby enabling it to handle regional variations, accents, and dialects with ease. By addressing current challenges and leveraging emerging technologies, AI linguistics can elevate the voices of all communities, regardless of their size or linguistic characteristics, and thus ensure that every language flourishes in the digital age.
