Multi-Microphone Speech Recognition Implementation

Implementation of speech recognition from multiple microphones Multi-microphone speech recognition is used in meeting rooms, teleconferencing systems, and industrial scenarios. The goal is to obtain a clear signal from each speaker using spatial processing of the microphone array. ### System Components The full stack includes: 1. Beamforming — directional signal amplification from the desired direction 2. Acoustic Echo Cancellation (AEC) — echo cancellation from speakers 3. Noise Reduction — noise reduction 4. Speaker Diarization — separation by speakers 5. STT — final transcription ### Beamforming with Py

Audio + SciPy

import numpy as np
from scipy.signal import correlate

class DelayAndSumBeamformer:
    def __init__(self, mic_positions: np.ndarray, sample_rate: int = 16000):
        self.mic_positions = mic_positions  # (n_mics, 3) координаты в метрах
        self.sample_rate = sample_rate
        self.speed_of_sound = 343.0  # м/с

    def compute_delays(self, direction: np.ndarray) -> np.ndarray:
        """Вычисляем задержки для каждого микрофона"""
        delays = np.dot(self.mic_positions, direction) / self.speed_of_sound
        delays -= delays.min()
        return (delays * self.sample_rate).astype(int)

    def beamform(self, signals: np.ndarray, direction: np.ndarray) -> np.ndarray:
        """signals: (n_mics, n_samples)"""
        delays = self.compute_delays(direction)
        output = np.zeros(signals.shape[1])
        for i, delay in enumerate(delays):
            output += np.roll(signals[i], -delay)
        return output / len(delays)
```### Commercial SDKs for multi-microphone processing For production, we recommend using specialized libraries: - **Microsoft Audio Stack (MAS)** — built into Azure Cognitive Services - **WebRTC Audio Processing Module** — open-source, C++ with Python bindings - **ReSpeaker SDK** — for ring microphone arrays (6-mic circular) - **STFT-based MVDR beamformer** (librosa + scipy) — research-quality ### Microphone arrays | Configuration | Directionality | Scenario | |-------------|----------------|----------|
| Linear 4-mic | 1D | Conference table | | Circular 6-mic (ReSpeaker) | 360° | Round table | | Planar 8-mic | 2D | Ceiling installation | ### Integration with diarization After beamforming, we use pyannote.audio to separate speakers:```python
from pyannote.audio import Pipeline

pipeline = Pipeline.from_pretrained(
    "pyannote/speaker-diarization-3.1",
    use_auth_token="YOUR_HF_TOKEN"
)

diarization = pipeline("beamformed_output.wav", num_speakers=4)
for turn, _, speaker in diarization.itertracks(yield_label=True):
    print(f"{speaker}: {turn.start:.1f}s - {turn.end:.1f}s")
```### Integration with hardware solutions Tested devices: - **ReSpeaker 4/6-mic USB Array** — plug-and-play, Ubuntu/Windows - **miniDSP UMA-8** — professional array, XMOS DSP - **JABRA PanaCast 20** — conference support with SDK ### Implementation times - Basic beamforming + STT: 1 week - With AEC and noise reduction: 2 weeks - Full system with diarization and dereverberation: 3–4 weeks