Audio Recognition

Where It's Applied

In my practice, I apply this system to process audio content of any origin: from corporate meeting recordings to phone calls, webinars, and conferences. The system automatically converts speech to text format with complete preservation of context and semantics, analyzes the emotional tone of statements, identifies conversation participants, and generates structured protocols for subsequent archiving and intelligent search.

Who Will Benefit

I recommend this solution to corporate departments that conduct regular meetings and need to document decisions without manual note-taking. Contact centers gain a tool for analyzing service quality and clients' emotional states in real time. Law firms can automate consultation documentation, while research centers can archive interviews and discussions with full-text search capabilities.

Technologies

Core Recognition Stack

As the main engine, I use Whisper from OpenAI — a model trained on 680 thousand hours of multilingual audio. It supports 99+ languages and handles accents, background noise, and technical terminology well. The key point: I select the model size (tiny, base, small, medium, large) dynamically based on accuracy requirements and available resources. For accuracy-critical tasks, I combine Whisper with regional models like Yandex SpeechKit or Google Cloud Speech-to-Text for enhanced reliability.

Local deployment avoids API latency and preserves data confidentiality — everything is processed on your infrastructure through Python.

Speaker Diarization and Voice Separation

One of the most challenging tasks is separating the audio stream by participants. I use pyannote.audio, based on deep neural networks for speaker diarization. The system can separate voices from each other even with speech overlap, which is critical for meetings with multiple participants. The model is trained on real-world data and performs well on phone speech and video conferences.

Emotional Tone Analysis

I apply specialized models for speech emotion recognition, which analyze not just text but also speech prosody: intonation, pace, volume, and timbre. This allows determining the speaker's confidence level, irritation, enthusiasm, or hesitation. In contact centers, this enables real-time service quality assessment, while in meetings, it reveals participants' true feelings about discussed topics, regardless of their words.

Audio Preprocessing

Before sending the signal for recognition, I apply a series of processing steps: volume level normalization (LUFS), removal of constant background noise (spectral subtraction, noise gates), and noise suppression (advanced noise suppression algorithms). Adaptive filters remove low-frequency hum and high-frequency artifacts typical of phone communication. In practice, this increases recognition accuracy by 10-15% for low-quality audio.

Precise Time Synchronization

The system preserves accurate time stamps for each speech segment (timestamp-accurate transcription), allowing direct linking of text to moments in the original recording. Metadata includes participant information, statement start and end times, recognition confidence score, identified language, and dialect. This is essential for comfortable navigation through lengthy recordings.

Post-Processing and Document Structuring

Recognition results undergo post-processing algorithms to correct systematic errors: contextual correction (word-based corrections using context), number and time normalization, punctuation restoration. I apply domain-specific rules to correct professional terminology. Text is structured in protocol format with paragraph separation, case restoration, and punctuation added based on speech patterns.

Processing Architecture and Scaling

The entire stack is built on Python using librosa for audio processing and PyTorch for neural model operations. I implement the API layer with FastAPI for maximum performance. The system supports parallel processing of multiple files through task queues (Celery or RQ), enabling horizontal scaling of throughput. In my infrastructure: deployment on NVIDIA GPU (CUDA) provides 10-50x speedup depending on the model. One GPU can simultaneously handle multiple audio streams, efficiently utilizing parallelism.

Typical configuration: on an RTX 3090, the system processes a one-hour recording in 3-5 minutes of real-time processing, including all diarization and emotion analysis stages.

Important Organizational Considerations

During implementation, I always emphasize careful attention to confidentiality — all audio recordings must be stored according to company security policies and GDPR (if dealing with European data). I insist on establishing a verification process: automatically generated protocols must be reviewed by a proofreader before final archiving, especially for legally significant documents. A critical point is configuring recognition parameters to match specific languages, dialects, and professional terminology (legal terms, medical concepts, etc.). In practice, this may involve fine-tuning models on your own data. It's important to plan storage capacity for audio recording archives and processed texts — with large volumes, this can be a significant expense.