“Learning another language is like becoming another person” wrote Haruki Murakami.
Languages bring people closer as a community. Languages help coordinate ambitious enterprises that mankind has built right from ancient pyramids to modern financial systems. Language is crucial for investigation because it takes proof of communication to establish guilt. Whether it’s the testimony of the suspect or conspiracy by the criminals, languages are key in delivering justice. Understanding the criminal’s mindset takes effort. Investigation requires immersion in the culture, lifestyle and mindset of the accused. Language is a key component because words shape thoughts. Thoughts shape action. Every crime is usually accompanied by communication and execution. In government agencies dealing with organized crime, radicalization and cross-border terrorism, language skills is the critical factor that differentiate average officers from excellent investigators.
In a linguistically diverse country like India, language is a challenge for the agencies because most citizens are bilingual. While courts deliver justice in the local language of jurisdiction, Hindi or English, criminals necessarily don’t speak the language of the court. Despite English education spreading across the country, a vast majority of Indians aren’t still conversant in English. In such a scenario, multilingual transcription is a basic necessity for every investigator and court. Inability to understand a language should never be a barrier to justice. Swift transcription services for crimes with links in multiple states is needed for assisting investigators and judiciary. While external intelligence agencies require real-time transcription for intelligence gathering and threat assessment from foreign soil, internal security personnel need the capabilities for maintaining law and order.
For instance: an insurgency group in Maharashtra borders operating in multiple states may converse in Marathi Hindi & English apart from local dialects. Translation requires AI systems that can deal with a mix of words so that they can decipher the context, intention and meaning of the words.
Traditional automatic speech recognition (ASR) are built around three tightly coupled components namely a) Acoustic model that maps sound into phonemes, b)Pronunciation lexicon that converts phonemes into words c) Language model that predicts word sequences. Typically, systems are trained on one language and rely on fixed vocabularies. In real world, bi-lingual people switch seamlessly and even mix languages without qualms and still communicate well with fellow humans. It creates problems at multiple level as code-switching breaks traditional ASR by violating core assumptions like one language, stable pronunciation and predictable grammar.
Apart from code-switching, forensic teams often deal with low-quality recordings, multiple speakers, emotional tone like stress, whispering, or shouting. Each speaker may switch language differently and pronunciation varies by region and education.
In forensic contexts (legal evidence, surveillance, intelligence), Transcripts must be accurate and defensible. Errors can misidentify intent, misattribute statements and alter timelines or locations. Codeswitching also introduces systematic bias as certain languages get under-recognized and words with multiple meanings in different context often get mistranscribed.
Systematic bias arises because some languages lack training datasets for AI. The shortage is structural, and it becomes especially acute in forensic AI, where data requirements are stricter than in general AI. Forensic AI needs very specific kinds of data. It should be legally obtained recordings with clear speaker attribution, time-stamped data and metadata (location, device type, noise conditions). Casual data like YouTube audio, calls, social media may be used for AI LLM training but often lacks consent for legal use, chain-of-custody documentation and ground-truth transcripts. The training datasets shortage is a structural problem that can’t be addressed at the AI level but needs structural solutions.
Even after addressing the training datasets problem, the problem of pronunciation difference persists. A native American speaker’s English has different phonetic pattern than that of South Asian speakers pronouncing English words. Even in the English-speaking world, there is divergence. Eg: British English sounds different than American English. The issue is further complicated by rare-event problem in forensic scenarios. Forensic AI isn’t just about everyday speech. Forensics labs deal with rare events like threat languages, covert conversations, stressed speech and whispering. It is context-specific speech that is hard to simulate authentically.
Cross-border investigations within a State or between countries remain tricky domain for forensic science as AI continues to advance. Multi-lingual transcription will be key to completing investigations in a timely manner.
