The ASR speech-to-text cloud service uses a deep learning speech recognition engine deeply optimized for Chinese, capable of accurately handling various accents, speaking speeds, and professional terminology. The system supports MP3, WAV, M4A, FLAC, and other common audio formats, and can handle both real-time streaming and batch file processing modes to meet diverse transcription needs.
Combined with LLM natural language processing, the system not only outputs verbatim transcripts but also automatically adds punctuation, segments paragraphs, identifies speakers, and generates meeting summaries with key point annotations. For data security, all audio files are encrypted in transit and immediately deleted after processing, ensuring your confidential conversations and enterprise data remain protected. Ideal for enterprise meeting records, customer service quality analysis, legal proceedings transcription, and media subtitle production.
Supports both real-time streaming speech recognition and batch file processing modes. Real-time mode generates verbatim transcripts synchronously during meetings or calls; batch mode allows uploading large volumes of audio files at once, with the system automatically scheduling processing and notifying upon completion. Both modes dramatically reduce the time from audio to text, letting teams focus on content analysis rather than tedious dictation work.
The recognition engine is deeply trained and tuned for Chinese (Mandarin and Taiwanese accents), accurately handling complex speech content including Chinese-English mixing, professional terminology, numbers, and addresses. It also supports multilingual recognition for English, Japanese, and other languages, continuously optimizing the model through customer scenario data to maintain high recognition accuracy across different industries.
All audio files are transmitted via TLS encryption and stored in isolated environments during processing, with original audio files automatically deleted upon transcription completion according to customer settings. The system retains no customer voice data and does not use data for model training. On-premise deployment options are available, keeping highly confidential conversation content entirely within the enterprise's internal network for complete data sovereignty.
The system has built-in Speaker Diarization technology that automatically identifies multiple speakers and labels them 'Speaker A / B / C', precisely distinguishing each speaker's content in multi-person meetings or interview scenarios. Combined with automatic paragraph segmentation and timestamps, meeting records become instantly clear, making it easy to search and reference specific speaking segments.
After transcription is complete, the system automatically applies large language model intelligent post-processing: adding punctuation, correcting verbal filler words, generating meeting summaries and action item lists. It can also perform keyword annotation, sentiment analysis, or topic classification for specific needs, transforming verbatim transcripts into directly usable structured business insights.
Supports direct upload recognition for mainstream audio formats including MP3, WAV, M4A, FLAC, OGG, and AAC without pre-conversion. Handles audio content from phone recordings, video conference recordings (with automatic audio track separation), podcast files, and surveillance recordings, with an API interface for seamless integration with existing enterprise systems.
Real-Time Streaming + Batch Processing
Chinese / English / Japanese Multilingual Recognition
Encrypted Transmission, Deleted After Processing
MP3 / WAV / M4A / FLAC
