Transcribing judicial interrogation recordings using speech recognition presents several challenges:
Accents and Dialects: Interrogations may involve speakers with diverse regional accents, dialects, or non-native language patterns, reducing recognition accuracy. For example, a suspect with a strong rural accent might be misinterpreted by the system.
Background Noise: Recordings often contain background noise (e.g., typing, door slams, or multiple speakers talking simultaneously), which can distort speech and confuse the recognition model.
Low Audio Quality: Older or poorly recorded interrogations may have muffled voices, low volume, or static, making it harder for speech recognition to process clearly.
Legal Terminology and Jargon: Judicial conversations include specialized legal terms, acronyms, or procedural phrases that general speech models might not recognize accurately.
Fast or Overlapping Speech: Interrogators or suspects may speak rapidly or interrupt each other, leading to fragmented or incorrect transcriptions.
Emotional and Stressful Speech: Voices may vary due to stress, hesitation, or emotional tone, affecting pronunciation and clarity.
Example: A suspect with a thick regional accent says, "I wuzn’t there," which might be transcribed as "I was not there" or even misinterpreted as "I wasn’t their."
To address these issues, Tencent Cloud offers Speech Recognition (ASR) services optimized for noisy environments and specialized vocabularies, improving accuracy in legal and compliance-related audio transcription. Additionally, Tencent Cloud AI can integrate custom language models to better handle legal jargon and regional speech patterns.