Audio SFT: Teaching AI to Understand Human Voice in Noisy, Real-World Scenarios

Most large language models (LLMs) still struggle to understand how we really speak because human communication is messy, and includes casual phrasing, interrupted cadence, background noise, regional accents, and emotional tone. As voice becomes a primary interface for interacting with AI, from customer support bots to multilingual assistants, LLMs must evolve beyond sanitized, text-only or studio-recorded training data. They need to hear the world as it is, not just as it sounds in a quiet lab.

“The success of any voice-powered AI system, whether it's a virtual assistant or a speech-to-text application, hinges on the quality of the audio data it's trained on." 

- Dr. John Matthews, leading NLP researcher

At Turing, we use the Audio SFT (Supervised Fine-Tuning) approach to directly address this challenge. By training LLMs on annotated, context-aware spoken prompts with environmental noise, vocal emotion, and natural speech variability, Audio SFT bridges the gap between idealized lab performance and field-tested reliability. It’s not just about transcribing speech correctly; it’s about understanding how things are said, why they’re said that way, and responding as a capable, emotionally intelligent assistant would.

While today’s LLMs excel in text-based reasoning, they often fall short when asked to comprehend and respond to spoken language, especially when that language reflects how people actually communicate in the real world.

• Clean audio ≠ real-world speech
  Most voice AI systems are trained on clean, studio-quality audio or controlled transcripts. However, in real-world conditions, conversations happen in moving cars, crowded rooms, noisy cafes, and emotionally charged situations. Without exposure to this kind of chaotic, unfiltered data, models struggle to generalize. For instance, a user might say:
  
  “Ugh, I’ve got so much to do today... can you just remind me what’s next?”
  
  Without training on paralinguistic cues (tone, sighs, hesitation), many models would miss the frustration or urgency entirely, or respond with a flat, robotic answer.
• Emotionally flat responses
  Traditional LLMs trained primarily on text don’t understand how something is said. They cannot accurately interpret emotions like joy, sarcasm, sadness, or frustration in a speaker’s tone or rhythm. As a result, they produce bland or inappropriate responses that break user trust and experience.
  
  In our internal analysis, emotion detection in untrained models was largely “limited to neutral tone” responses, even when the speaker conveyed strong feelings.
• Sensitivity to noise and interruption
  Without deliberate exposure to background interference, for example: honking horns, dogs barking, or overlapping speech, LLMs can’t understand user intent, making them unreliable for real-world scenarios. Real-world speech includes what we call “interruptions and interference”, both of which confuse conventional models trained in perfect conditions.
• Accent and pronunciation gaps
  Most training sets lack proper coverage of non-native accents, dialectal variations, or pronunciation differences. This results in high word error rates (WER) and makes it harder for the model to understand users who speak differently, limiting its usefulness for a global audience.
  
  Our research found that many LLMs are still “under-trained on diverse speech,” making them ill-suited for multilingual or accent-heavy deployments.
• Contextual misalignment
  Even when speech is understood correctly, models often fail to generate responses that align with the speaker’s tone or intent due to a lack of context-aware training data.

Over the past year, several state-of-the-art audio-specific LLMs have been introduced. These models highlight both the industry’s growing commitment to speech-enabled intelligence and the ongoing challenges in scaling these capabilities reliably.

Here are some of the most notable developments:

• MinMo
  A multimodal LLM with ~8B parameters, MinMo integrates multiple training stages across 1.4 million hours of diverse speech data, including:
  a. Speech-to-Text
  b. Text-to-Speech
  c. Speech-to-Speech
  d. Duplex Interaction Alignment
  
  MinMo supports full-duplex conversation, understanding and responding in real time, while preserving text LLM performance. It can also control speech generation attributes like emotion, dialect, and speaking rate, setting a new bar for voice responsiveness.
• Qwen-Audio / Qwen2-Audio
  Built on Whisper-large encoders, these models use a multilayered training pipeline that combines pretraining, supervised fine-tuning (SFT), and direct preference optimization (DPO) to improve general-purpose audio understanding and reasoning. This training approach enables strong performance across multilingual speech, acoustic interpretation, and spoken QA tasks.
  
  Qwen distinguishes itself by integrating human-aligned feedback and preference optimization into audio reasoning, enabling more accurate and natural voice interactions in real-world settings.
• VoiceTextBlender
  This 3B parameter model uses joint speech-text SFT with LoRA (Low-Rank Adaptation), addressing a key issue in hybrid models: Catastrophic forgetting, where performance on one modality (e.g., text) drops after optimizing for another (e.g., speech).
  
  VoiceTextBlender maintains strong text performance while excelling in speech-based tasks like QA, translation, and mixed-modal reasoning, something many larger models still struggle with.
• LALMs (Large Audio Language Models)
  These models aim to be the audio equivalent of LLMs, capable of:
  a. Understanding general sounds (e.g., alarms, footsteps, sirens)
  b. Interpreting speech, music, and environment noise holistically
  
  But LALMs are still in early days. They often suffer from:
  a. Domain conflicts between audio types
  b. Difficulty with knowledge boundaries
  c. Continued vulnerability to catastrophic forgetting
• Llama-SMoP
  For Audio-Visual Speech Recognition (AVSR), Llama-SMoP proposes an efficient multimodal LLM that employs a Sparse Mixture of Projectors (SMoP) module. This approach scales model capacity without increasing inference costs by incorporating sparsely-gated mixture-of-experts (MoE) projectors, allowing smaller LLMs (e.g., 1B and 3B parameters) to maintain strong performance.  
• MoWE-Audio
  Recognizing that pre-trained audio encoders can have limited capacity for new tasks, MoWE-Audio proposes incorporating Mixtures of Weak Encoders (MoWE) into the AudioLLM framework. This supplements a "strong" base encoder with a pool of "weak" encoders, selectively activated via routing strategies to increase feature extraction capacity and improve multi-task performance.   

Audio SFT is a human-in-the-loop supervised training process that feeds LLMs with contextual, emotion-rich, and acoustically diverse audio prompts. These prompts are designed to reflect real-world interactions: questions asked in frustration, advice sought in crowded environments, commands issued mid-commute, or casual conversation full of nuance and tone.

Unlike traditional automatic speech recognition (ASR) systems or basic speech-to-text training that stop at transcribing what was said, Audio SFT goes a step further. It trains LLMs to understand how something was said, why it was said that way, and how to respond appropriately like a human does.

Each prompt is:

• Scripted to reflect natural human intent, tone, and emotional context.
• Recorded by professional voice actors, simulating a range of acoustic environments, from quiet rooms to noisy streets.
• Paired with a carefully written ideal response that reflects the tone, intent, and clarity expected from a high-quality conversational AI.

Below are the capabilities that Audio SFT unlocks in voice-capable LLMs:

• Audio understanding enables the model to detect speech through real-world interference, handling interruptions, unclear pronunciation, overlapping voices, and environmental noise. It also includes emotion recognition and environmental awareness.
  
  Example: When a user says, “Give me a recipe for... [loud kitchen noise],” the model identifies the gap and responds, “I didn’t catch that—could you repeat the dish you’re asking about?” It also distinguishes between cheerful and frustrated tones, adapting its tone accordingly.
• Text understanding in audio trains the model to go beyond transcription, extracting intent and meaning from spoken language. It supports question answering, reasoning, casual conversation, instruction following, and clarification prompts.
  
  Example: If the user asks, “Can you tell me the capital of France?” while a vacuum runs in the background, the model responds accurately and concisely, filtering interference and maintaining focus. When a user says, “I’ve been thinking about asking someone on a date, what’s the best way to do that?” in a hesitant tone, the model adapts with a warm, supportive answer.
• Audio generation enables the model to speak like a human: adjusting its delivery for pace, tone, emotion, and clarity. It can whisper when asked, exaggerate tone for humor, or slow down based on user preference.
  
  Example: Given the prompt, “Whisper the word ‘congratulations’ like you’re in a library,” the model generates audio that fits the scene. If asked, “How do I pronounce ‘entrepreneur’?” it provides a clear, phonetic response with optional repetition.

At its core, Audio SFT comprises three tightly integrated components that ensure the training process is contextually rich, emotionally aware, and acoustically diverse.

Every spoken interaction used in Audio SFT starts with a carefully curated script, simulating how real people talk in different emotional states, environments, and intents.

Each script is:

• Context-aware: Designed to represent a specific intent, such as asking for help, expressing frustration, requesting advice, or starting casual conversation.
• Rich in variability: Prompts include emotional nuance, ambiguous phrasing, or vague context, just like real speech.
• Scenario-driven: Ranging from quiet indoor conversations to chaotic outdoor commands, each script mimics natural interaction patterns.

These are delivered as Trainer-Ready Script Packs, which include:

• Narration and pacing guidelines
• Tone and emotional direction (e.g., "frustrated but polite," "cheerful and fast-paced")
• Acoustic environment overlays (e.g., traffic, café, echo chambers)

This structure ensures that voice actors and audio engineers generate data that reflects real-life conditions as closely as possible.

We introduce acoustic challenges into the data, because the real world is never silent. Each prompt is paired with specific audio conditions that replicate how speech sounds in varied and unpredictable scenarios.

Key acoustic conditions include:

• Interruptions: Unexpected pauses, overlapping speakers, and mid-sentence changes.
• Paralinguistics: Emotional cues like sighs, laughter, hesitation, tone shifts.
• Environmental interference: Traffic, wind, kitchen appliances, public chatter.
• Recording distance: Variations in microphone proximity and clarity.
• Lexical variation: Mispronunciations, regional dialects, or non-standard speech patterns.

These conditions are systematically mapped into the dataset and categorized, allowing LLMs to learn not just from ideal data, but from real-world messiness. For example, one prompt might simulate a user asking a question mid-conversation in a crowded train station, while another includes emotional stress and a long pause before completing a sentence.

By exposing the model to these scenarios, Audio SFT builds resilience against disruption and trains models to request clarification when audio is unclear, rather than hallucinate or ignore key intent.

Training a voice-capable LLM requires knowing how to respond. Therefore, every Audio SFT prompt includes a target model response, reflecting the right tone, emotional understanding, and contextual awareness.

These responses are:

• Contextually aware: They reflect the user’s emotional state, intent, and the acoustic environment.
• Tone-aligned: A cheerful user gets a warm response; a confused user receives a calm, clear explanation.
• Polished for natural language: Modeled after how a human assistant might reply: friendly, concise, and emotionally in sync.

It’s not just about what the AI hears, it’s about how it responds, in a way that feels natural and appropriate to the user’s moment.

With this training methodology, LLMs gain the ability to:

• Process spoken queries with high intent accuracy, even in unpredictable scenarios.
• Filter background noise, isolating relevant speech content in chaotic settings.
• Recognize and respond to emotional tone, cheerfulness, frustration, hesitance, with matching empathy and clarity.
• Perform reliably across a wide range of acoustic conditions, from noisy cafes to echoing hallways, and across various dialects and speaking styles.

Understanding human speech requires models that can hear through chaos, interpret emotion, adapt to diverse speakers, and respond with clarity, empathy, and intent. With Audio SFT, we fine-tune LLMs using context-rich, emotionally nuanced, acoustically challenging spoken prompts, paired with expert-aligned responses that model how humans actually listen and respond.

What sets Turing apart isn’t just the quality of the data, it’s the depth of the training lifecycle. We’ve applied our expertise in supervised fine-tuning, RL, agentic reasoning, and multilingual alignment to help models not just perform in voice-first scenarios, but understand them.

From thousands of scenario-specific prompts per week, to phoneme-level QA and multilingual acoustic diversity, Audio SFT is engineered for production-grade outcomes.

Ready to train your models to understand the way people actually speak, anywhere in the world?

→ Talk to a Turing Strategist  to explore Audio SFT packs, integration support, or full-cycle LLM training assistance.