Every step of a founder's journey deserves a record.Founders · AI · PolicyCHINAOPC / ENGLISH EDITION

SenseVoiceSmall: Turning Chinese Recordings into Checkable Work Materials

SenseVoiceSmall can recognize speech in multiple languages such as Chinese and output some sound event information. This article takes the collection of interview materials as an example to explain the division of labor in transcription, terminology correction, original sound positioning and summarization, and reminds the team not to regard emotion labels as people's true psychological judgments, nor to directly apply the language coverage of the model family to the Small model.

Chinese edition published 23 Oct 2026 · 阅读中文原文 ↗

Source timingVerified version: SenseVoiceSmall; the official model card will be released in July 2024, and this article is a basic introduction; the data will be verified on October 23, 2026.

Capability boundaries of Small models

According to the official model card of FunAudioLLM, SenseVoiceSmall supports speech recognition in Mandarin, Cantonese, English, Japanese and Korean, and has language recognition, speech emotion recognition and audio event detection capabilities. Model family and model-specific language coverage cannot be mixed, and Small should be selected based on its own instructions.

These capabilities can help organize recordings, but transcribed text, emotion tags, and background sound tags are different types of information. Sound events such as applause and laughter cannot automatically indicate the attitude of a sentence, nor can we judge the speaker's true psychology or recognition of a certain business based on just one label.

Keep the original words first when organizing the interview (ChinaOPC analysis)

A common requirement is to organize authorized customer interviews into searchable material. You can keep the original recording first, segment it into segments of appropriate length, and then check the transcription and terminology. Product names, numbers, negative words and time expressions need to be reviewed carefully, because errors in these contents will change the meaning of the subsequent summary.

The workbench should allow users to return from text to the corresponding audio clip. Segment numbers, original files, and start and end positions are maintained by the processing pipeline, and it cannot be assumed that the model has provided precise verbatim timestamps or speaker identities. When multiple people are talking, the attribution of the speech should be additionally checked and marked manually if necessary.

The summary cannot replace the original record

If a meeting conclusion or action list is also required, place the summary in a separate step, clearly marking it as coming from the transcribed material. Leave unspecified owners, deadlines, and commitments to be confirmed rather than filling them in with common sense. Users should see both the original basis and the edited summary.

Corrections within the same sentence should also preserve semantics. For example, "Not this week, but due next week" cannot only capture the two keywords "this week" and "delivery". During the sampling review, such corrections, hesitations and rhetorical questions can be specially added to check whether the sorting process turns uncertain matters into definite conclusions.

Deliver a maintainable material library

A small team can provide transcribed texts, glossaries, snippets to be confirmed, and search directories, and count manual review time into the service cost. File permissions, retention periods, and deletion methods should be agreed at the beginning of the project to prevent recordings from being copied to accounts unrelated to the project.

This article does not have actual measurement of recognition accuracy or processing speed, nor does it use emotion recognition for personnel evaluation. Whether it is applicable should be verified using authorized samples that are similar to the customer's recording conditions, rather than based solely on a demonstration in a quiet environment.

Further reading

Understand real-time voice service access; Compare European language subtitles. For more introduction, please see AI Model Column.

Sources and editorial notes

AI-assisted organizing and writing. The model information is based on the official information at the end of the article; the application process, task examples and acceptance suggestions are original analyzes by ChinaOPC. The model performance evaluation has not been run, and the hypothetical project has not been written as a delivered case. English translation of the Chinese edition, prepared with machine assistance and editorial checks. Source verification dates refer to the original Chinese research.

Source titles and publisher names are retained in their original language. Refer to the linked originals for official documents and current requirements.

← Back to AI ModelsEditorial standards ↗