Many users who record conversations in regional dialects end up with scattered audio files that are hard to locate, edit, or share with teammates. Standard transcription tools often cannot parse local accents, forcing users to replay recordings repeatedly and manually type notes, which creates disjointed records stored separately on phones, laptops and cloud drives.

Traditional transcription tools are built primarily for standard Mandarin. When participants shift to dialects, these tools produce garbled text or skip content entirely. Manual transcription takes extra time, and the resulting notes are difficult to sync and share later.

MeetingMinutes dialect recognition is a speech-to-text feature designed for regional Chinese accents. Before recording or uploading audio, users select the target dialect from the supported list. The system transcribes spoken dialect into readable text. It supports more than 20 dialects such as Cantonese, Sichuanese, Shanghainese, Henan and Hunan dialects. Note that recognition performance can drop under heavy background noise or frequent switching between multiple dialects in one session.

A local business outreach interview shows how this fits into the workflow. The interviewer opens MeetingMinutes, picks the correct dialect option and starts recording. Once the conversation ends, the transcript saves to the file library. The user can access the transcript on the web portal, sync it across devices, export it to a usable format, or send it to colleagues via QR sharing. All transcript and audio assets stay organized inside the same workspace, rather than scattered in separate folders.

This feature fits field researchers, local sales teams and community coordinators who regularly hold conversations in dialects. Users who need advanced custom glossary control or specialized niche accents will need complementary workflows. As part of MeetingMinutes, the transcript works with the platform’s native file management, cross-device sync and team sharing functions to keep all meeting materials centralized.