Real-time AI: Audio

Analyzing spoken interactions for both content and acoustic nuances.

Processing Audio in Real-time

For interactions involving speech, Blankstate employs a sophisticated high-speed audio pipeline to capture live sound and prepare it for real-time analysis. This pipeline is optimized for minimal delay, ensuring that audio data enters the system efficiently.

Interactive Audio Analysis Demo Coming Soon!

This section will feature an interactive demo powered by BKS. You'll be able to provide audio input (e.g., via microphone or file upload) and see a simulated analysis from Blankstate's Core AI. This will demonstrate how the system processes both the transcribed text and acoustic features, showing how the blend of these data streams leads to richer insights, multi-dimensional Protocol scores, and inferred tone/sentiment, highlighting the unique capabilities of our self-supervised approach.

[ coming soon ]

Dual Data Streams: Text and Acoustics

To gain a comprehensive understanding of spoken communication, Blankstate simultaneously generates two distinct streams of "raw data" from the live audio:

The Transcribed Text: The spoken words are converted into text using a fast and accurate Speech-to-Text (STT) model. This stream captures *what* was said.
The Acoustic Features: The raw audio signal is concurrently processed using techniques like spectrography and acoustic analysis. This step extracts detailed quantitative descriptors of the sound waves, such as frequency components, pitch contours, loudness variations, and timing. This stream captures crucial information about *how* the words were spoken, including tone, emotion, and emphasis.

This dual-stream approach ensures that the system doesn't rely solely on the literal transcript but also incorporates the rich paralinguistic information present in the audio.

Feeding the Core AI Model with Blended Data

The combined, rich set of raw data – the transcribed text and the detailed acoustic feature profile – is precisely what is fed into our Core AI Model. The Intention Blended Framework (IBF) utilizes the Core AI, accessed via the Analysis Input function, to process this blended input.

Our Core AI Model is specifically designed to process both the linguistic content (text) and the paralinguistic cues (acoustic features) together. By analyzing these streams in conjunction, the model can infer the user's underlying intent, sentiment, tone, and other subtle nuances that go far beyond a simple word-for-word transcript. This ability to blend and learn from diverse data types is a core strength of our self-supervised approach, enabling a much deeper and more accurate understanding of complex human communication.

Latency and Prediction

While processing audio introduces slightly higher latency compared to pure text analysis, Blankstate targets a latency of 1 to 4 seconds for audio-based interactions. To help compensate for this, prediction mechanisms can be applied downstream within the system. These predictions can help course-correct the direction of the interaction or prepare responses while the full audio analysis is completed, aiming to maintain a fluid user experience.

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