AI Audio Intelligence Built for Ethiopian Languages
Raw speech-to-text is just the first step. Andebet turns spoken audio in Amharic, Afaan Oromo, Tigrinya, and English into searchable knowledge, structured themes, and verifiable acoustic evidence.
45 free minutes on signup · Telebirr & CBE Birr supported · Instant export to DOCX, SRT, VTT & JSON
Multilingual Fieldwork Synthesis (Horn of Africa Research)
Amharic, Oromo & English • Multi-Language Corpus • 02:10
የፕሮጀክቱ implementation phase በሁለት regional states ውስጥ በተሳካ ሁኔታ ተጀምሯል።
“The project's implementation phase has successfully commenced across two regional states.”
Hawaasni naannoo keessumattuu dargaggoonni carraa hojii uumameefitti baay'ee gammadoo dha.
“The local community, particularly the youth, are very pleased with the employment opportunities created for them.”
What is AI Audio Intelligence for Ethiopian Languages?
AI Audio Intelligence is an enterprise technology layer that extends beyond basic speech-to-text to understand, organize, and synthesize spoken audio recordings across multilingual environments. In the Horn of Africa, where everyday business, academic research, and media conversations constantly blend Amharic, Afaan Oromo, Tigrinya, and English, generic transcription engines produce fragmented, error-filled transcripts without context. Andebet's Audio Intelligence platform integrates four foundational systems: dedicated regional acoustic recognition with native Ge'ez and Qubee orthography, multi-speaker diarization with neural clustering, automatic code-switching detection, and semantic audio search. This enables researchers, enterprises, and newsrooms to ask complex qualitative questions across hundreds of hours of spoken audio and receive answers grounded in verifiable audio timestamp citations with zero hallucination.
Audio Intelligence Platform Capabilities
How Andebet's regional language intelligence platform compares against generic global transcription APIs.
| Capability / Evaluation | Andebet AIDedicated Model | Whisper (large-v3) | Google Speech | Manual Typing |
|---|---|---|---|---|
Acoustic & Language Tuning Designed specifically for Ethiopian vocal tracts, phone audio, and everyday conversational speech | Purpose-built for Ethiopian speech; robust on phone audio and voice notes | General multilingual model; Amharic is a low-resource language in training data | Pre-trained primarily on formal broadcast and search speech (am-ET) | Natural human comprehension, but subject to transcriber fatigue and backlog |
Ge'ez Fidel & Orthography Handling authentic fidel characters, homophones, and Ethiopic punctuation (፡፣።) | Native Ge'ez fidel with punctuation; context-aware homophone handling | Frequently outputs Latin transliteration or drops low-resource words | Basic Ge'ez fidel output; limited punctuation and formatting | Authentic fidel, though spelling consistency varies by individual transcriber |
Multi-Speaker Diarization Separating and labeling distinct speakers across interviews, panels, and meetings | Built-in automated speaker separation with turn labeling and timestamps | Not included natively (requires external Python pipelines like PyAnnote) | Available as cloud add-on; inconsistent on overlapping or conversational speech | Accurate human identification, but very tedious to log timestamps manually |
Amharic + English Code-Switching Handling fluid mid-sentence transitions between Amharic and English words | Automatic bilingual switching; preserves English technical terms and Ge'ez text | Prone to hallucinating or omitting English terms in Amharic audio | Requires primary language lock; struggles when speakers switch mid-sentence | High quality if transcriber is fluent in both languages |
Regional Accents & Phone Audio Performance on compressed phone voice notes, meetings, and regional accents | Trained on real-world mobile voice notes, phone calls, and regional variations | Requires clean, high-bandwidth studio microphone audio for best results | Best on clean standard broadcast speech; degraded on noisy mobile audio | Adapts to local accents if transcriber is familiar with the region |
Turnaround Time Time required to convert a 1-hour recording into an editable transcript | A few minutes per audio hour; ready for synchronized interactive review | Depends on local GPU setup; typically 10–15 minutes on cloud APIs | Fast cloud API processing (typically 5–8 minutes) | Typically 4 to 6 hours of manual typing per 1 hour of audio |
Billing & Accessibility Payment options and accessibility for Ethiopian researchers and businesses | Web workspace + API with support for Ethiopian Birr (ETB) and global cards | Developer API billed in USD with international credit cards only | Enterprise GCP console setup required; billed in USD | Freelance rates (typically 500–1,500+ ETB per audio hour) |
The Four Pillars of Andebet Audio Intelligence
An integrated stack designed to take you from unorganized recorded audio to verified operational decisions.
Native Acoustic Recognition
High-accuracy neural acoustic and language models built for Semitic and Cushitic phonology, Ge'ez fidel syllabary, and Qubee Latin script.
Speaker Diarization & Identity
Acoustic clustering separates multiple voices in complex environments—meetings, radio panels, focus groups, and field recordings.
Code-Switching Detection
Urban Ethiopian conversations fluidly blend Amharic, Oromo, Tigrinya, and English. Our models detect script transitions mid-sentence.
Semantic Audio Search & RAG
Query your recording library in plain language and receive synthesized answers backed by clickable audio timestamps for zero hallucination.
How Audio Intelligence Transforms Your Work
From raw recordings to actionable intelligence in four streamlined stages.
Capture & Upload Audio
Upload smartphone voice notes, Zoom meeting recordings, field interview tapes, or stream live audio via API.
Supports all major audio formats
Acoustic & Semantic Processing
Our neural pipeline transcribes speech, separates speaker turns, identifies languages, and generates vector embeddings.
Processes 60 mins in under 2 mins
Query & Synthesize Knowledge
Search across recordings, generate executive summaries, cluster qualitative themes, and ask complex cross-interview questions.
Interactive AI workspace
Export & Automate
Download verified Word/DOCX reports, qualitative NVivo files, video subtitles, or integrate via REST API and MCP agents.
Developer & enterprise ready
Who Relies on Andebet Audio Intelligence?
Trusted by researchers, journalists, humanitarian agencies, and developers across East Africa.
Qualitative & Field Researchers
Universities, anthropologists, think tanksAnalyze hundreds of hours of recorded fieldwork interviews and focus groups with verified audio evidence and NVivo export.
Newsrooms & Investigative Journalists
Broadcasters, independent media, podcastersExtract verified, time-coded quotes and generate broadcast subtitles in Ge'ez fidel and Latin scripts in minutes.
Humanitarian & Development Agencies
UN agencies, international NGOs, evaluatorsDocument community feedback and qualitative beneficiary assessments with parallel English translation for donor reports.
Developers & Enterprise IT
Fintech, call centers, AI software buildersIntegrate native Ethiopian speech-to-text, audio search, and voice agents into applications using our production REST API.
Frequently Asked Questions: Audio Intelligence
What sets Andebet apart from generic speech-to-text tools.
Traditional transcription merely outputs raw text from an audio file. Audio Intelligence goes much further: it separates speakers, identifies languages and code-switching dynamically, indexes semantic themes, extracts structured insights, and allows users to query entire libraries of audio with verifiable audio timestamp citations.
Global models treat African languages as low-resource afterthoughts. They lack training on native Horn of Africa acoustic phonemes, confuse Ge'ez fidel homophones, miss Afaan Oromo vowel lengthening and consonant gemination, and fail completely on rapid colloquial code-switching between Amharic and English. Andebet was built from the ground up on native acoustic data to solve these specific linguistic challenges.
Andebet currently supports Amharic (አማርኛ), Afaan Oromo, Tigrinya (ትግርኛ), and Ethiopian English, with automatic code-switching detection between these languages. Additional Horn of Africa languages are in active research and development.
Yes! Andebet offers an official Model Context Protocol (MCP) server that enables Claude Desktop and autonomous AI agents to search your audio archives, inspect transcripts, and retrieve verified audio clips directly within your agent workflows.
Explore Andebet's Platform Capabilities
Audio Transcription Platform
High-accuracy speech-to-text with multi-speaker diarization and export.
FeatureSemantic Audio Search
Query spoken audio archives in natural language with millisecond citations.
APIDeveloper REST API
Integrate Ethiopian speech recognition and audio search into your app.
IntegrationModel Context Protocol (MCP) Server
Connect your audio archives directly to Claude and AI agent workflows.
AnalyticsData Studio & Visualizations
Interactive visual analytics of speaker turns, vocabulary, and sentiment.
Turn recorded Ethiopian conversations into searchable truth.
Join researchers, media networks, and global organizations extracting structured knowledge from Amharic, Afaan Oromo, and Tigrinya audio.
- 45 free minutes included
- No credit card required
- Instant API & Workspace access
- Enterprise data sovereignty