Voice recording analysis turns a recording into a transcript, speaker labels, sentiment, and topics automatically, so you search it instead of replaying it. Speak reads the words, the voice behind them, and what is on screen, on every file you upload.
The same audio analysis engine, pointed at whatever your team already records.
Every interview transcribed, coded, and searchable across a study, instead of read once and filed away.
Score every recorded call against your own rubric instead of sampling a handful by hand. See call scoring.
Episode transcripts, topics, and clips pulled from audio and video files alike. See video analysis.
Depositions and intake calls turned into structured, searchable records your team can trust.
Session recordings scored against a rubric, so coaching does not depend on someone re-watching the tape.
Quick voice notes indexed alongside long recordings in the same searchable archive.
From a recording to a searchable, structured file, in three steps.
Drop in an audio or video file, record directly in the app, or connect a meeting bot, an embed, or a phone line.
Pick a transcription engine suited to the audio, then keyword extraction, sentiment analysis, topic detection, and named entity recognition run automatically.
Clean up any line in the átiratszerkesztő, then ask AI Chat questions across one file or your whole library. Insights roll up into shareable dashboards, and every transcript, summary, and analysis exports to PDF, Word, CSV, or JSON for reports and handoffs.
A raw transcript still takes a person to read start to finish. Speak runs keyword extraction, sentiment analysis, topic detection, and named entity recognition on every recording automatically, and you can compare the output side by side in the szövegelemző eszköz. Word clouds and frequency analysis surface the language your speakers actually use, and batch upload processes hundreds of files in one pass. Ask questions across your whole library with multi-model AI Chat, and let AI Agents run recurring audio workflows, from customer call analysis to podcast repurposing, without manual steps.
To analyze an audio transcription, run it through speaker identification, keyword extraction, sentiment analysis, and topic detection instead of reading it line by line. Speak does this automatically on upload, turning the text into fields you can search, filter, and compare.
A átirat is a text version of what was said. That is a useful starting point, but it stops short of analysis. Real audio analysis identifies who spoke and when, extracts the keywords and topics that matter, detects the emotional tone of the conversation, and recognizes the people, organizations, and products mentioned, then connects all of it across a full library of recordings so patterns show up that a single file never reveals.
Most teams that adopt an audio tool stop at the transcript and wonder why the payoff feels thin. The value sits in the structured data pulled from the text, and in querying that data across dozens or hundreds of recordings at once, not in the text on its own.
Topic detection is the fastest route: upload the files and the model clusters every recording by theme automatically. Instead of listening to hours of audio, you scan a short list of topics and open only the recordings and timestamps that match.
Speak reads a file on three layers at once: the words that were said, the voice behind them (tone, energy, and pacing), and the on-screen or visual patterns pulled from it, so one upload gives a full picture instead of a transcript alone. MI-ügynökök can run this across a batch automatically, so a research team or a support desk does not open each file by hand.
Accuracy is table stakes; most serious platforms clear it in 2026. The real differences sit in the analytics layer, the AI, and how the platform handles scale. Can you upload 200 files at once and get results back in hours? Can you search the entire library by keyword, speaker, or topic? Can you ask a model to compare themes across a full study? Multiple transcription engines let a team optimize for accuracy across languages and recording conditions, and AI Chat, powered by Claude, Gemini, and GPT, answers questions across one recording or the whole archive. Developers who want programmatic access connect the same pipeline to Claude, ChatGPT, and Cursor through Speak’s MCP server, with no custom integration required.
Academic researchers use it to code qualitative interviews at scale, and piackutatás teams run it across focus groups and interview panels the same way. Beszédanalitika teams monitor call center quality and track sentiment across thousands of calls a month. Journalists search hours of recorded interviews for a specific quote or claim. Product teams aggregate voice-of-customer feedback across hundreds of conversations. The common thread: audio once considered too time-consuming to analyze systematically is now a structured, queryable data source.
Sales calls, support calls, and coaching sessions are audio files like any other. The same engine that reads tone and topics can grade them against your rubric.
Objections, next steps, and deal risk scored on every call your reps already record.
Every support call graded on greeting, empathy, and resolution instead of a manual sample.
Managers see exactly where a call went off script, instead of scrubbing through the recording to find it.
No terminal, no npm, no config. Speak’s MCP server gives any assistant 100+ tools to search, analyze, and act on every recording in your library in about 60 seconds, the same layer your workspace already runs on, wired into hundreds of apps through an integrations layer and a full developer API.
Real feedback from teams using Speak to analyze recordings, calls, and interviews.
Run the transcript through speaker identification, keyword extraction, sentiment analysis, and topic detection instead of reading it line by line. Speak does this automatically on upload, turning the text into fields you can search and compare.
Upload the files and let topic detection cluster every recording by theme automatically. You scan a short list of topics instead of listening to each file, then open only the recordings and timestamps that match.
Not directly in its standard interface. Voice mode handles live spoken conversation in one session, but it does not accept uploaded audio files for structured analysis. Speak transcribes and analyzes uploaded audio, then connects that data into ChatGPT through MCP.
Not from OpenAI directly. No single tool runs transcription, speaker labels, sentiment, and topic detection the way a dedicated audio analysis platform does. Speak is built for that: upload a file and get all four automatically.
Gemini can process audio inside Google’s own apps, but it does not run speaker identification, keyword extraction, or topic detection on your recordings. Speak runs all of that automatically, then lets you query the results from Claude, ChatGPT, or Gemini.
AI can identify tone, pacing, and energy in a voice recording, not only the words spoken, which is different from understanding sound the way a person does. Speak reads that layer alongside the transcript on every file you upload.
Audio analysis software processes audio recordings to extract structured data and insights. Basic tools stop at transcription. Speak goes further with speaker identification, keyword extraction, sentiment analysis, topic detection, named entity recognition, and AI Chat across your entire library, turning unstructured audio into data your team can act on.
Speak supports all major audio formats, including MP3, WAV, M4A, FLAC, OGG, WMA, AAC, and WebM. You can also upload video files and Speak extracts and analyzes the audio track, with no need to convert anything before uploading.
Accuracy depends on audio quality, background noise, number of speakers, accents, and terminology. Speak offers multiple transcription engines so you can pick the one suited to your recording conditions. Most users see accuracy above 95% with clear audio, and Speak supports 100+ languages.
Yes. Speak supports transcription and analysis in over 100 languages, selected manually or detected automatically. Keyword extraction, sentiment analysis, and topic detection work across supported languages, which suits multinational research, global call analysis, and multilingual content teams.
Yes. Every file uploaded to Speak is transcribed, indexed, and full-text searchable by keyword, speaker, date, topic, or folder across your entire recording history, and AI Chat answers natural language questions across any group of files at once.
Most audio tools stop at transcription. Speak adds NLP analytics, multi-model AI Chat, batch processing, and a searchable archive: multiple transcription engines instead of one, Claude, Gemini, and GPT for analysis, and automatic keyword extraction, sentiment analysis, topic detection, and named entity recognition on every file.
Book a free consult, bring a real recording, and watch it transcribed, analyzed, and searchable before the call ends.
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