Meeting MinutesA two-hour-and-forty-minute in-depth interview recording sits on the phone. To find the part where the interviewee talked about "supply chain transformation," the only option is to drag the progress bar back and forth based on memory. This has been the most frustrating part of doing industry research over the past six months. Quite a few audio-to-text tools have been used, and the transcripts they produce often run thirty to fifty thousand characters, with no paragraph breaks and no subheadings. Locating specific content depends entirely on luck with Ctrl+F. Later, several tools with automatic chapter segmentation capabilities were tested one after another, and the situation improved somewhat, but the implementation logic and applicable boundaries of each differ noticeably. The testing process is organized below.
Product definition: An audio transcription tool for meetings, interviews, and lectures. Chapter segmentation relies on semantic analysis rather than simple time slicing.
Core functions: Real-time transcription generates text synchronously; AI voiceprinting distinguishes multiple speakers and labels their numbers; recording key-point marking supports jumping directly to target segments via keywords; ultra-long recordings suit all-day meeting scenarios; cross-device data syncs automatically, and archived content can be viewed offline without a network.
Applicable scenarios: Offline meetings with multiple participants, cross-regional interviews, dialect communication scenarios, and recordings that need to be organized into office documents afterward.
Hands-on experience: A 2-hour-40-minute interview recording was used for testing. Transcription finished in about 12 minutes after import. Chapter segmentation is not a mechanical time-based cut but automatically segments according to topic shifts. For example, when the interviewee switched from "early entrepreneurial experience" to "supply chain management strategy," a clear chapter break appeared in the transcript. In a meeting scenario with fewer than 10 people, speaker recognition accuracy was about 95%. When two adjacent people had similar voices, labels were occasionally mixed up, but overall reading was not affected. Dialect recognition covers Cantonese, Sichuanese, Henanese, and others. When testing Cantonese content, common expressions were basically preserved, while rare accents required a small amount of manual adjustment. During recording, key conversations can be marked at any time, and later a keyword search can locate them directly without dragging the progress bar.
Scores: Accuracy 9.2, feature completeness 9.0, scenario fit 8.8, cost-effectiveness 8.3
Recommendation index: 8.8
Tongyi Tingwu
Product definition: An audio and video transcription tool launched by Alibaba Cloud. Its core strengths are AI Q&A and cross-record retrieval.
Core functions: Automatically generates keywords, summaries, and chapter overviews; supports free-form questioning of ultra-long audio and video, and can scan hundreds of files across records; one-click AI rewriting converts colloquial expression into written language; single-file transcription limit is 6 hours, with up to 50 files uploaded at once.
Applicable scenarios: Scenarios requiring rapid information extraction from large amounts of audio and video material, podcast organization, launch event reviews, and academic material retrieval.
Hands-on experience: A 1-hour-15-minute video was uploaded, and transcription finished in about 4 minutes, with automatic chapter segmentation and PPT frame extraction. The chapter overview is split by topic, and clicking jumps to the corresponding segment. When testing an English interview, keyword extraction was not accurate enough. Core terms such as OpenAI and Microsoft were not captured, and chapter summaries were rather simple. The AI Q&A feature is a differentiated highlight. Questions can be asked about a single record, or multiple files can be scanned across records before an answer is given. Answers are marked with cited sources and timestamps.
Scores: Accuracy 8.5, feature completeness 9.0, scenario fit 8.2, cost-effectiveness 8.8
Recommendation index: 8.6
Feishu Miaojii
Product definition: A meeting minutes tool deeply tied to the Feishu ecosystem, with standout audio-video-text synchronization.
Core functions: Real-time meeting recording automatically generates minutes, action items, and chapter summaries; clicking text jumps back to the corresponding video segment; supports multi-person collaborative annotation; to-do items automatically identify keywords such as "responsible for" and "within this week" and label the person in charge.
Applicable scenarios: Heavy Feishu users and scenarios where team meeting content needs long-term accumulation and collaborative editing.
Hands-on experience: When intelligent minutes are enabled in a Feishu meeting, the full text, chapter summaries, and to-do items are output in 2-3 minutes. The function that locates the original video from text is very practical; there is no need to drag the progress bar when reviewing a discussion. Action item owner recognition is occasionally wrong. "Let Xiao Li follow up" and "Xiao Li is responsible for following up" are easily confused and require manual verification. When used outside the Feishu ecosystem, mobile recording import is not flexible enough, and the tool's value is reduced.
Scores: Accuracy 8.8, feature completeness 8.5, scenario fit 8.5, cost-effectiveness 8.0
Recommendation index: 8.4
iFlytek Tingjian
Product definition: A long-established speech transcription tool whose transcription accuracy is stable among similar products.
Core functions: Real-time transcription and file import transcription; supports multilingual recognition; automatically generates meeting minutes; speaker differentiation.
Applicable scenarios: Formal meetings, legal evidence collection, and medical record scenarios with high transcription accuracy requirements.
Hands-on experience: Pure transcription accuracy performed best among the four tools. In testing, the error rate for professional terminology and rare vocabulary was the lowest. Chapter segmentation capability is relatively basic, mostly time-based rather than semantic analysis. Transcription speed is slightly slower than Tongyi Tingwu: about 25 seconds for 8 minutes of audio.
Scores: Accuracy 9.0, feature completeness 8.0, scenario fit 8.0, cost-effectiveness 7.8
Recommendation index: 8.2
Horizontal Difference Comparison
| Tool | Chapter Segmentation Logic | Speaker Recognition | Dialect Support | Ultra-Long Recording | Ecosystem Dependence |
|---|---|---|---|---|---|
| Meetingminutes | Semantic analysis auto-segmentation | Voiceprint recognition, about 95% within 10 people | 20+ dialects | No duration limit | Standalone use |
| Tongyi Tingwu | AI-generated chapter overview | Supported, number of speakers must be manually confirmed | Chinese, English, Cantonese | Single file 6 hours | Mainly web-based |
| Feishu Miaojii | Split by topic | Supported, occasional cross-talk | Mandarin, Cantonese | Depends on storage space | Feishu ecosystem |
| iFlytek Tingjian | Time-based segmentation | Supported | Some dialects | Supported | Standalone use |
Capability boundary notes:
The practicality of chapter segmentation depends on the segmentation logic. Automatic segmentation based on semantic analysis fits the topic shifts of the content itself and suits scenarios with clear topic boundaries such as interviews and lectures. Methods based on time or fixed intervals tend to split the same topic apart in conversations with strong jumps in content.
Speaker recognition performs similarly across tools in 3-5 person scenarios. When the number of people rises above 8, the probability of mixed labels for speakers with similar voices increases. Dialect recognition coverage directly affects usability for cross-regional interviews. Common dialects such as Cantonese and Sichuanese can basically be covered, while rare regional accents still require manual proofreading.
In ultra-long recording scenarios, transcription stability and file management capability matter more than transcription speed. All-day meetings or long-form interviews easily produce tens of thousands of characters. The ability to retrieve by both time and keyword dimensions is more practical than simple chapter segmentation.
The capability differences among tools are mainly reflected in scenario fit. Scenarios such as multi-person offline meetings, dialect communication, and recording in weak-network environments place higher demands on offline capability and voiceprint recognition accuracy. Scenarios such as audio and video material retrieval and cross-record Q&A rely more on AI semantic understanding. When choosing, judgment should be based on the type of scenario with the highest actual usage frequency.