Upload any audio file and Speak transcribes it, identifies speakers, extracts keywords, detects sentiment, and surfaces topics automatically. Turn interviews, calls, podcasts, and recordings into searchable, analyzable data your team can act on.
Upload audio from any source, connect recording tools through Zapier, and export transcripts and analytics to the platforms your team already uses.
Most audio tools stop at transcription. Speak goes further with speaker identification, keyword extraction, sentiment detection, topic modeling, and AI Chat that lets you query any recording or your entire audio library at once.
Upload audio in any major format and Speak transcribes it automatically. Choose from multiple transcription engines to get the best accuracy for your language, accent, and recording conditions. Supports MP3, WAV, M4A, FLAC, OGG, and more.
Speak detects and labels individual speakers throughout each recording. Know exactly who said what in interviews, calls, and group discussions. Speaker labels carry through to transcripts, analytics, and exports for easy attribution.
Automatically identify the most important terms and phrases in every audio file. Speak surfaces recurring keywords, industry terms, and significant concepts so you can quickly understand what each recording covers without reading the full transcript.
Detect emotional tone across the conversation. Speak's audio sentiment analysis identifies positive, negative, and neutral segments, giving you a clear picture of how participants felt throughout the recording. Track sentiment shifts over time or across batches of files.
AI identifies what was discussed and when throughout each recording. Topic modeling surfaces the key themes covered in every audio file, making it easy to navigate long recordings, compare discussions across files, and spot recurring patterns in your data.
Speak automatically identifies people, places, organizations, products, and other named entities mentioned in your audio files. Use entity data to build structured indexes of your recordings and quickly find references across your library.
Get a visual representation of key themes and the most frequently used terms across your audio files. Word clouds and frequency counts help you spot patterns at a glance and communicate findings to stakeholders who prefer visual summaries.
Ask questions about any single recording or across your entire audio library. Powered by Claude, Gemini, and GPT models, AI Chat lets you extract quotes, compare themes, summarize findings, and generate reports without reading every transcript line by line.
Every audio file you upload is transcribed, indexed, and full-text searchable. Find any conversation, keyword, or speaker mention across your entire library. Build an organized, queryable archive of all your audio recordings over time.
Simple transcription tools give you a text file. Speak gives you a full analytics layer on every audio file you upload. Here is what sets Speak apart from basic audio-to-text converters.
Transcription is just the starting point. Speak automatically runs keyword extraction, sentiment analysis, topic detection, and named entity recognition on every audio file. You get structured, analyzable data from every recording without any manual effort.
Different recordings need different engines. Speak offers multiple transcription providers so you can choose the best accuracy for your language, terminology, and audio quality. Academic interviews, noisy field recordings, and phone calls each benefit from different engine strengths.
Query your entire audio library at once. Ask AI Chat to compare themes across 50 interviews, find every mention of a specific topic, or summarize patterns across months of customer calls. This is cross-file analysis that single-recording tools simply cannot do.
Speak gives you access to Claude, Gemini, and GPT for different analysis needs. Research coding, executive summaries, and exploratory questioning each benefit from different model strengths. You choose the right model for each task instead of being locked into one.
Upload hundreds of audio files at once and Speak processes them all. Batch transcription and NLP analysis means you can analyze an entire study, an archive of customer calls, or a season of podcast episodes in a single workflow rather than one file at a time.
Set up AI Agents to automatically process incoming audio files, generate reports, extract key findings, and distribute insights to your team. Automate the repetitive parts of audio analysis so your team can focus on interpretation and decision-making.
Researchers, analysts, journalists, and teams across industries use Speak to turn audio recordings into structured, actionable data. Here is how different teams put audio analysis to work.
Upload qualitative interviews and Speak transcribes with speaker attribution, then runs NLP analytics across all participants. Use AI Chat to code themes, extract quotes, and compare responses. Built for the rigor that academic, UX, and market research demands.
Analyze sales calls, support recordings, and customer feedback sessions at scale. Track sentiment trends, identify common objections, spot product mentions, and surface patterns across hundreds of calls. Give your CX and sales teams data they can act on.
Transcribe podcast episodes, extract key topics and quotes, and identify the most engaging segments. Use AI Chat to generate show notes, social media clips, and blog content from your episodes. Turn every recording into multiple content assets.
Record lectures, workshops, and training sessions, then make them searchable and analyzable. Students and trainers can search for specific topics, review key segments, and extract structured notes from hours of recorded content.
Transcribe depositions, hearings, and compliance recordings with speaker labels and timestamps. Search across recordings for specific statements, entities, or topics. Create a searchable, auditable archive of every recorded interaction.
Capture ideas, observations, and notes in the field, then upload to Speak for transcription and analysis. Voice memos become searchable text with keyword extraction and topic detection, turning scattered recordings into organized, retrievable knowledge.
Create a free Speak account and upload audio files in any major format. You can also record directly in the platform or connect your calendar to capture meeting audio automatically. Batch upload is supported for large file sets.
Select the transcription engine that works best for your audio quality and language. Speak supports 100+ languages and offers multiple engines so you can optimize for accuracy based on your specific recording conditions and terminology.
Once uploaded, Speak transcribes your audio and automatically runs keyword extraction, sentiment analysis, topic detection, named entity recognition, and speaker identification. No manual setup required. Every file gets the full analytics treatment.
View analytics dashboards for individual files or across your entire library. Use AI Chat to ask questions, compare themes, extract quotes, and generate summaries. Choose between Claude, Gemini, or GPT models depending on the analysis you need.
Export transcripts, summaries, and analytics to Word, CSV, PDF, or SRT. Share files and insights with your team through shared folders and permissions. Connect with Zapier and other tools to build automated workflows around your audio data.
Organizations are sitting on enormous volumes of untapped audio data. Customer calls, research interviews, internal meetings, training sessions, podcast episodes, and field recordings all contain valuable insights that never get extracted. The recordings exist, but the information inside them remains locked away because nobody has time to listen to hundreds of hours of audio and manually take notes.
AI-powered audio analysis has changed this. What used to require dedicated analysts with specialized tools is now accessible to any team. Upload a batch of audio files, and modern platforms transcribe, tag, and analyze them automatically. The barrier to working with audio data has dropped dramatically, and the organizations that take advantage of this are finding competitive insights their competitors are still leaving on the table.
Transcription gives you a text version of what was said. That is a useful starting point, but it is not analysis. Real audio analysis goes several layers deeper. It identifies who spoke and when. It extracts the keywords and topics that matter. It detects the emotional tone of the conversation. It recognizes the people, organizations, and products mentioned. And it connects all of this across your full library of recordings so you can spot patterns that are invisible when you look at one file at a time.
The distinction matters because most teams that adopt audio tools stop at transcription and wonder why the ROI feels limited. The value is not in the text itself. The value is in the structured data you extract from the text, and in the ability to query and compare that data across dozens or hundreds of recordings. That is what separates a transcription tool from an audio analysis platform like Speak.
When evaluating audio analysis tools, accuracy is table stakes. Every serious platform achieves strong transcription accuracy in 2026. The real differentiators are the analytics layer, the AI capabilities, and how well the platform handles scale. Can you upload 200 files at once and get results back in hours? Can you search across your entire library by keyword, speaker, or topic? Can you ask an AI model to compare themes across a full research study? Can you choose different transcription engines and AI models based on what works best for your specific audio?
Speak is built for teams that need this depth. Multiple transcription engines let you optimize for accuracy across different languages and recording conditions. NLP analytics run automatically on every file. AI Chat powered by Claude, Gemini, and GPT lets you query individual recordings or your entire library. And AI Agents automate repetitive workflows so your team can focus on interpretation rather than processing.
The use cases for audio analysis keep expanding. Academic researchers use it to code qualitative interviews at scale. Speech analytics teams use it to monitor call center quality and track customer sentiment. Journalists use it to search through hours of recorded interviews for specific quotes and claims. Product teams use it to aggregate voice-of-customer feedback across hundreds of user conversations. The common thread is that audio data, once considered too time-consuming to analyze systematically, is now a structured data source that teams can query, compare, and act on.
"We went from weeks of qual analysis to one day. Easy to use, easy to implement, and the support has been incredible."
Connor H. Data Analyst, G2 review
"High accuracy, multilingual support, and insightful analysis. Integrations with Google and Zapier make it easy to streamline everything."
Volker B. COO, G2 review
"I used to spend 45-30 minutes transcribing notes. Now it's done in seconds, and I'm writing in minutes."
Ted H. Business Owner, G2 review
"I use Speak in French and English for meetings up to two hours. It saves time and increases the precision of my reports."
Francois L. Financial Advisor, G2 review
"It joins meetings, records, documents, and summarizes. I don't miss important points and it saves me a ton of time."
Ercan T. Business Development, G2 review
"It's easy to use, and I can actually get in contact with the team behind the product. Valuable to speak to a real human."
Markus B. Medical Director, G2 review
Common questions about audio analysis software, transcription accuracy, and how Speak handles different types of audio files.
Audio analysis software is a platform that processes audio recordings to extract structured data and insights. Basic audio analysis tools provide transcription. Advanced platforms like Speak go further with speaker identification, keyword extraction, sentiment analysis, topic detection, named entity recognition, and AI-powered querying across your entire audio library. The goal is to turn unstructured audio into searchable, analyzable 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 will extract and analyze the audio track. There is no need to convert files before uploading. Speak handles format conversion automatically during processing.
Transcription accuracy depends on audio quality, background noise, number of speakers, accents, and technical terminology. Speak offers multiple transcription engines so you can choose the one that delivers the best results for your specific recording conditions. Most users see accuracy above 95% with clear audio. For challenging recordings, you can select engines optimized for noisy environments or specific languages. Speak supports 100+ languages.
Yes. Speak supports transcription and analysis in over 100 languages. You can select the language before processing, or let Speak detect it automatically. NLP features including keyword extraction, sentiment analysis, and topic detection work across supported languages. This makes Speak well-suited for multinational research projects, global customer call analysis, and multilingual content teams.
Transcription converts speech to text. Audio analysis extracts structured, actionable data from that text. With Speak, every audio file is automatically processed for speaker identification, keyword extraction, sentiment analysis, topic detection, and named entity recognition. You also get AI Chat to query recordings, dashboards to visualize patterns, and the ability to search and compare across your entire audio library. Transcription is the foundation. Analysis is where the insights come from.
Yes. Every audio file uploaded to Speak is transcribed, indexed, and full-text searchable. You can search by keyword, speaker, date, topic, or folder across your entire recording history. You can also use AI Chat to ask natural language questions across any group of files, such as "What did participants say about pricing across all interviews this quarter?" This cross-file search capability is one of the most valuable features for teams working with large audio datasets.
Yes. Speak's multiple transcription engines include options optimized for noisy environments, phone calls, and multi-speaker recordings. Speaker identification (diarization) labels each speaker throughout the recording so you can see exactly who said what, even in group discussions with overlapping dialogue. For best results with challenging audio, you can select the transcription engine that performs best for your specific conditions.
Most audio tools focus on transcription alone. Speak is a full audio analysis platform that includes transcription, NLP analytics, multi-model AI Chat, batch processing, and a searchable archive. Key differences include: Speak offers multiple transcription engines instead of one. Speak provides Claude, Gemini, and GPT models for AI analysis. Speak runs automatic keyword extraction, sentiment analysis, topic detection, and named entity recognition on every file. And Speak's AI Chat works across your entire library, not just individual recordings. For teams that need more than a transcript, Speak provides the analytical depth that basic tools do not.
Upload your recordings and get automatic transcription, speaker identification, keyword extraction, sentiment analysis, and AI Chat across your entire library. Every plan includes the full analytics suite.
Create a free account, upload your first audio files, and see transcription and NLP analytics in action. Get full access to AI Chat and dashboards during your 7-day trial.
Need help setting up audio analysis workflows for your organization? We help teams configure batch processing, build custom reporting, and integrate Speak into existing research or analytics pipelines. Book a consult to get started.
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