Bland Babel: Achieving Low-Latency, Noise-Robust Transcription on A100 GPUs

Delivering real-time voice transcription is a balancing act between speed and accuracy. Bland Babel’s engineering team faced this challenge head-on, aiming to minimize the delay before transcription begins while maintaining high fidelity—even when audio is noisy. This journey led them to optimize every layer of their system, from GPU kernels on NVIDIA A100s to clever audio preprocessing. The result is a transcription service that not only responds in fractions of a second, but also stays remarkably accurate in chaotic real-world environments. Along the way, the team is already envisioning how these transcriptions can feed directly into larger language models, setting the stage for seamless voice-driven AI interactions. What follows is a look into the key technical strategies that make all this possible, woven into a narrative of innovation and forward-thinking design.

Navigating Multilingual Chaos with Precision

The assumption that a transcription system can neatly process one language at a time is a luxury that real-world applications can’t afford. In AI phone calls, there are no convenient drop-down menus for language selection. People speak how they want—sometimes switching languages mid-sentence, sometimes blending two into a fluid hybrid like Spanglish. The task of handling this linguistic entropy in real-time is where Bland Babel has been engineered to excel.

Multilingual transcription is not just a matter of supporting multiple languages. It requires tackling four core challenges:

1. Language identification without context – When no prior knowledge of the speaker’s language is available, the system must determine what is being spoken on the fly. Unlike written text, where scripts often provide clues (e.g., Cyrillic for Russian, Hangul for Korean), spoken language offers no such safety net. The first few syllables of a conversation could belong to any of hundreds of languages. A wrong assumption at the start can send the entire transcription spiraling into nonsense.
2. Code-switching and blended languages – Unlike hard language switches (where someone finishes speaking English and starts speaking French), code-switching happens within the same sentence, sometimes in unpredictable ways. Common in multilingual communities, it introduces context-dependent meaning, where a phrase might shift in significance depending on whether the next few words remain in the same language or not.
3. Homophones and false cognates across languages – Some words have identical or near-identical pronunciation across multiple languages but mean completely different things. For example:
   
   • Gift (English: present, German: poison)
   • Pan (Spanish: bread, English: cooking tool)
   • Rat (German: advice, English: rodent)
4. A transcription system that ignores the linguistic context of surrounding words can easily output the wrong meaning.
5. Maintaining fluency in a mixed-language transcript – Even if the transcription correctly identifies multiple languages, the challenge remains of presenting the output in a way that respects the speaker’s intent. In code-switched conversations, certain phrases remain in the original language because they convey a meaning that doesn’t translate cleanly. Bland Babel must determine whether to transcribe a foreign phrase as-is or attempt a transliteration—an area where many transcription models fail.

Solving Language Identification in Real Time

When a user starts speaking, the system has milliseconds to determine which language models should be active. Bland Babel employs a hybrid approach that combines acoustic modeling, phoneme recognition, and confidence-weighted language embedding scores.

• Acoustic fingerprints: Different languages have distinct phonetic distributions. Even without understanding words, a model can determine which language is most likely based on how sounds are distributed. Japanese, for instance, has a far more constrained set of phonemes compared to English, meaning that the first few sounds spoken can provide strong hints.
• Statistical confidence across multiple models: Instead of hard-switching between languages, Babel runs a lightweight parallel inference step where multiple language models provide probability distributions for the incoming audio. A decision is then made dynamically:
  
  • If a single language dominates (>95% confidence), transcription proceeds in that language.
  • If multiple languages compete closely (e.g., 60% English, 40% Spanish), the system enters mixed-language mode, enabling code-switching handling.

This adaptive method allows Bland Babel to avoid false starts, a common problem where models incorrectly assume a language and only realize the mistake later in the conversation—by which point errors have cascaded.

Handling Code-Switching and Injected Terms

Code-switching presents an even more nuanced challenge. Words borrowed from another language don’t always signal a full transition—sometimes, they are deliberate insertions that carry context-dependent meaning.

For instance, in a sentence like:
"I need to take a pausa before I continue."
The Spanish word pausa means pause, but this doesn’t indicate that the speaker has fully switched to Spanish. The meaning of the phrase remains English-dominant.

Bland Babel prevents misclassifications by using confidence detection scores at the token level, allowing it to determine whether a word signals a language transition or a linguistic borrowing.

• Confidence-weighted classification: Each token receives a probability distribution across multiple languages. If a word is 85% likely to be Spanish but occurs in an English sentence, it’s treated as an injected term, rather than switching the entire model over to Spanish.
• Historical context weighting: The system doesn’t just look at the current word—it evaluates how frequently a speaker has switched in the past few seconds. A single foreign word in isolation is less likely to trigger a full language change than a sustained transition.
• Pronunciation modeling: Some words are borrowed across languages but pronounced differently. Consider “restaurant” in English versus the French “restaurant” (which drops the hard "t" at the end). Babel's phoneme-aware approach ensures that these are transcribed as the speaker intended, rather than defaulting to a monolingual assumption.

The Danger of Cross-Language Homophones

Some words are phonetically identical across languages but semantically unrelated, making them potential pitfalls for transcription systems. Take this simple phrase:
"He gave me a gift."

If a German-English bilingual speaker were speaking, should ‘gift’ be transcribed in English (as a present) or German (as poison)?

To resolve this, Bland Babel employs two key strategies:

1. Language priors from conversation history – If the previous few sentences were in English, and no German transition has been detected, the likelihood that gift should be transcribed as present is significantly higher.
2. Bilingual embeddings for semantic disambiguation – By leveraging multilingual word embeddings trained on cross-lingual corpora (such as XLM-R by Conneau et al., 2019), Babel can determine which meaning makes the most sense in context. If a phrase like "wrapped in a box" follows, it’s highly likely that gift refers to a present. If the surrounding context includes words like "dangerous" or "not safe to consume", the system weighs the German meaning more heavily.

Rendering a Mixed-Language Transcript That Makes Sense

Even when a system correctly identifies which words belong to which language, there's still the challenge of how to represent them in text. Code-switching is a real part of spoken communication, but many transcription models treat it awkwardly—either forcing a rigid translation (which distorts meaning) or leaving everything in its original language without punctuation or structure.

Bland Babel uses a linguistic cohesion model that aims to match the way a human would naturally write a bilingual conversation:

• Maintaining untranslated words where necessary: Some words simply don’t have a perfect translation ("hace frío" conveys more than just "it’s cold"). If the phrase carries meaning specific to its language, it remains in its original form.
• Applying natural code-switching conventions: If a speaker switches languages in a grammatically seamless way, the system avoids inserting unnecessary markers. However, if the transition is abrupt, it may denote the switch with a soft delimiter (e.g., a short pause).
• Ensuring readability: Unlike naïve multilingual transcriptions that produce a jumbled word salad, Bland Babel ensures that code-switched text reads naturally, balancing fidelity with clarity.

The result? A transcription system that doesn’t just recognize multilingual speech—it understands how to represent it accurately, preserving the speaker’s intent without introducing artificial constraints.

Racing to the First Token with A100 Optimizations

For Bland Babel, latency is the make-or-break factor. Users need to see the first transcribed words almost as they finish speaking. This Time To First Token (TTFT) – essentially how quickly the system produces the initial output – had to be as low as possible​. 

Achieving sub-100ms TTFT meant squeezing every drop of performance from NVIDIA’s A100 GPUs, known for their immense parallel horsepower. The team approached this on multiple fronts, ensuring the GPU spends its time doing useful work rather than waiting on data or overhead. Three key areas proved crucial: custom CUDA kernels, efficient memory usage, and smart batching.

Tuned CUDA kernels for speech. General-purpose deep learning frameworks are convenient, but they can introduce extra latency with layers that aren’t fully optimized for a specific task. By combining operations (for example, merging several small matrix multiplications or convolution steps into a single kernel launch), they reduced the per-token computation overhead. This cuts down the launch latency that accumulates when many tiny GPU tasks are invoked in sequence. The A100’s high core count and Tensor Cores were leveraged here – using mixed-precision math and Tensor Core instructions where appropriate to accelerate the neural network’s math without sacrificing accuracy. In practice, this meant the GPU could chew through audio features faster, as more of the work was being done in a single pass within each kernel call. The result is a leaner pipeline where the GPU stays busy doing real work rather than idling between kernels.

Making the most of memory bandwidth. A100 GPUs come with exceptionally fast on-board memory, and utilizing it effectively was another priority. The A100 offers high-speed HBM2 memory with up to 1.5 TB/s of bandwidth (about a 73% increase over the previous generation V100) and a massive 40 MB L2 cache for on-chip data​ (developer.nvidia.com)

Bland Babel’s model was tuned to take advantage of this by keeping the most frequently used data (like acoustic model weights or intermediate features) resident in GPU memory and cache as much as possible. Memory access patterns were carefully planned to be coalesced (so that threads fetch data in contiguous chunks) and to reuse data already loaded in the cache. The team also employed techniques like asynchronous memory prefetching – essentially telling the GPU to start pulling in the next chunk of audio data or model parameters before they’re actually needed. This hides memory latency by overlapping data transfers with computation. On the A100, the hardware supports copying data in the background while the Streaming Multiprocessors (SMs) execute other instructions​ (developer.nvidia.com). By orchestrating these overlaps, the GPU rarely has to stall waiting for data. In effect, the A100 is always fed with a steady stream of information to process​(developer.nvidia.com), preventing those thousands of CUDA cores from ever sitting idle. All of this careful memory management means the enormous memory bandwidth is efficiently used rather than wasted, directly translating to quicker processing of each audio snippet.

Batching without the wait. Throughput-wise, GPUs love batches – processing many audio streams together is more efficient than one at a time – but large batches introduce delay. Bland Babel found a sweet spot with micro-batching strategies that maintain high GPU utilization and keep TTFT low. Instead of waiting to accumulate a huge batch of audio from many users (which would make each user wait longer for their first token), the system intelligently forms small batches on the fly. The software groups incoming audio requests within very short time windows (mere milliseconds), just enough to let the GPU work on a few streams in parallel for efficiency. If only one audio stream is active, the system doesn’t stall at all – it processes that stream immediately (batch size of one). But if several requests arrive almost simultaneously, they get bundled so the GPU can crunch them together. This dynamic batching approach ensures that we get some of the throughput benefits of batching while adding negligible delay. In essence, Bland Babel’s server will never hold your audio waiting for others longer than a tiny fraction of a second. The A100’s architecture even allows executing multiple small kernels concurrently when resources permit, so two smaller batches can be in flight at the same time on different parts of the GPU. All of these tactics minimize wasted GPU cycles and get that first token out fast. The payoff is evident in the user experience: transcripts start appearing on-screen almost instantly, making interactions feel fluid and real-time.

By combining custom kernel optimizations, maximal memory throughput, and thoughtful batching, Bland Babel slashed the latency of its speech pipeline to unprecedented lows. These GPU-level optimizations work in concert – lean kernels keep compute efficient, memory tweaks ensure data is always on hand, and smart batching keeps the hardware busy – to deliver blazing-fast transcription. An utterance can be fed to the model and the first recognizable text can emerge in just a few hundred milliseconds end-to-end. In practical terms, conversations with the AI system feel natural because responses start with virtually no lag. This low-latency feat was a cornerstone in making Bland Babel’s service stand out, but speed alone isn’t very useful without accuracy. The next challenge was ensuring that those first tokens (and all subsequent ones) are correct, even when the input audio isn’t crystal clear.

Hearing the Signal Through the Noise

Real-world audio is messy. People talk over clattering keyboards, wind noise on phone mics, or chatter in the background. Bland Babel’s transcription engine needed to maintain high accuracy despite these common noise sources. Human listeners subconsciously filter out background noise; the system had to do the same. Two main strategies stick out: continuously estimating the Signal-to-Noise Ratio (SNR) of incoming audio, and dynamically adjusting frequency analysis to focus on the clearest parts of the signal. Together, these techniques act like giving the model a better ear in noisy environments.

SNR estimation and adaptive noise handling. The first step to tackling noise is knowing how noisy the audio is. Bland Babel’s pipeline monitors the audio input and computes an SNR estimate on the fly. In simple terms, SNR is the ratio of the useful speech signal to the background noise level. A high SNR means speech stands out clearly; a low SNR means the speech is buried in noise. By analyzing moments of silence or low speech energy, the system gauges the ambient noise floor, and by looking at when speech is present, it gauges the signal level. The result is a dB value indicating how challenging the audio might be. This matters because low SNR levels can dramatically decrease how accurately the system recognizes speech​. For example, an SNR of 30 dB (very little noise) is ideal, whereas at 0 dB (noise as loud as speech) even humans struggle. Knowing the SNR, Bland Babel can automatically adjust its transcription strategy. If the SNR is poor, the system might, for instance, apply more aggressive noise suppression algorithms before feeding the audio to the recognizer, or increase the gain on certain model features designed to detect speech presence. It’s a bit like automatically turning the “noise filter” knob up or down depending on the situation. This dynamic noise compensation helps prevent the garbage-in-garbage-out effect; it gives the model input that’s as clean as possible. Even in a multi-person meeting with lots of background murmur, the transcription engine remains usable because it’s actively compensating for the din in the background.

These noise-handling techniques significantly boost transcription accuracy in adverse conditions. Estimating SNR gives the system awareness – it knows when to be skeptical and apply aggressive cleaning. Dynamic frequency binning gives it agility – the ability to tune its “ears” to whatever conditions it encounters. Together, they ensure that Bland Babel doesn’t just work in a pristine lab setting with studio-quality audio, but also holds up in the wild. Testing showed that with these adaptations, word error rates dropped substantially in low-SNR scenarios. Even when background babble or static is present, the system captures the spoken words correctly far more often than it would with a static model. In high-SNR cases (quiet environments), the dynamic adjustments simply stay out of the way, so there’s no downside. Essentially, the system is always striving for an optimal representation of the speech: cleaning, filtering, and adapting as needed, just like a seasoned audio engineer tweaking knobs to get the best sound. With low latency and noise robustness in hand, Bland Babel’s transcription engine is both fast and reliable. But the team isn’t stopping there – they are looking at how these transcriptions can become part of something even bigger.

Transcription in the Loop: Embeddings and Future Inference Pipelines

Having a fast, accurate transcription is often just the first step in a larger AI workflow. In many applications, once you get the text from speech, you might want to feed it into a large language model (LLM) for higher-level understanding, summarization, or to act on the content of the speech. Bland Babel’s vision is to streamline this handoff by integrating the transcription engine more deeply with downstream LLM inference. Rather than treating speech recognition and language understanding as two separate siloed processes, they are exploring ways to merge them at the embedding level – effectively letting the LLM “listen” to the same features the ASR is using, without redundant processing. This forward-looking strategy could unlock even faster end-to-end voice interactions, where spoken words flow into actions or insights with minimal overhead.

Merging embeddings with LLMs. In a traditional pipeline, once speech is transcribed to text, that text is then re-encoded as input for a language model (for instance, turned into tokens and embedded as vectors) so the LLM can work with it. There is clearly some duplication here: the speech recognizer has already transformed raw audio into a rich internal representation (it had to, in order to decide on the words), but all that insight is discarded once plain text is produced, only for the LLM to start from scratch on the text. The idea of embedding merge is to cut out this redundancy. Bland Babel is investigating approaches where the latent representation of the audio – essentially the intermediate embeddings from the speech model – can be fed directly into a language model’s inference pipeline. One approach would be to train a small bridging network that takes the final-layer audio embeddings and transforms them into a form that a text-based LLM can understand. Another approach, inspired by recent research, is to use the speech model as a front-end and treat its output embeddings as a kind of prompt for a pre-trained LLM. In fact, studies have shown that an ASR system can be built by integrating a pre-trained speech model with a pre-trained LLM, where the LLM’s decoder generates text autoregressively when conditioned on speech representations.

In other words, the LLM is given a hint: “here is what the audio sounds like in embedding form, now complete the job and generate the transcript.” This kind of tight coupling means the LLM is not just getting a flat transcript to process; it’s getting a rich, nuanced vector representation of the speaker’s utterance, potentially carrying information about accents or audio context that plain text might miss.

Seamless pipeline, minimal trade-offs. The big advantage of integrating transcription embeddings into an LLM pipeline is efficiency – both in terms of time and information preservation. From a performance standpoint, eliminating the text re-encoding step saves processing. The LLM doesn’t have to devote time to reading raw text characters or tokens; instead, it ingests a ready-made embedding that came straight from the audio. This can shave significant milliseconds off the total response time, which is crucial when stacking systems (speech recognition + language understanding). It also means we avoid the latency of waiting for the full transcription to finish before starting the next step. In a merged setup, as soon as the speech model has processed a chunk of audio into embeddings, the language model can start doing its part. The pipeline becomes truly streaming end-to-end – imagine a scenario where while a person is still speaking, the LLM is already formulating a response or an action based on the partial utterance. That could make voice interfaces feel dramatically more responsive and interactive. Just as importantly, the richer information interface could improve accuracy and capability. Because the LLM sees the speech model’s “thought process” (the embeddings) and not just the final text, it might handle uncertainties or nuances better. For instance, if a word was unclear, the embedding might reflect ambiguity; a sufficiently integrated LLM could decide the word in context or even ask for clarification, rather than the ASR having to output an uncertain guess. This kind of synergy is what the Bland Babel team is looking toward. Crucially, all of this can be done without heavy performance trade-offs. Modern LLM architectures are flexible with input modalities – researchers have already fed audio and other modality prompts into text models with promising results​.

By designing the integration carefully (for example, ensuring the embedding dimensions and model capacities align), the addition of speech embeddings can be handled as just another input to the model, not a slow bolt-on. The computations can run on the same GPU (the A100 certainly has memory and compute to spare for a combined model) and even share some layers between the ASR and LLM parts in a multitask fashion.

While this embedding-level fusion of ASR and LLM is still in an exploratory phase, it represents a forward path with huge potential. Bland Babel’s fast and noise-robust transcripts would serve as more than just output – they become an input for deeper language reasoning systems in a way that wastes no time. Imagine dictating a complex request and getting an immediate, intelligent answer or action because the transcription and understanding happened almost as one unified process. That is the future vision: a pipeline where speaking to an AI is as seamless as talking to a human assistant, with the backend neural networks all working in concert in real-time. It’s an ambitious goal, but considering how far the team has pushed the boundaries of latency and accuracy already, it’s a natural next step.

In summary, Bland Babel’s engineering journey underscores a cohesive philosophy: optimize every piece (from GPU kernels to audio filtering) and then connect the pieces in innovative ways. By doing so, they’ve built a system that not only excels at its immediate task – transcribing speech swiftly and reliably – but is also poised to be a key component in larger AI interactions. Low-latency optimization on A100 GPUs ensures users aren’t kept waiting, advanced noise handling keeps the transcripts correct, and looking ahead, embedding-level integration hints that the best is yet to come. The result is a fluid, engaging user experience powered by deeply technical solutions under the hood, exactly the kind of marriage of engineering and usability that modern AI services strive for.

Conclusion

Bland Babel represents a huge leap forward in how we do transcription. From multiple languages to one — we are working to completely consolidate our transcription stack. The goal is to deliver a hyper optimized transcription model capable of handling every language seamlessly. 

While we are still early in our development, we hope Babel provides a delightful and unique experience that is at the cutting edge of the market. We hope you love it. 

Blandly,