Voice Rehabilitation

Speech Disorder Detection

Acoustic Biomarkers

import librosa
import numpy as np

class SpeechDisorderDetector:
    def extract_features(self, audio, sr=22050):
        y, _ = librosa.load(audio, sr=sr)
        features = {
            'jitter': librosa.feature.zero_crossing_rate(y)[0].mean(),
            'shimmer': np.mean(np.abs(np.diff(np.abs(y)))),
            'HNR': self._compute_hnr(y),
            'MFCC': librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13).mean(axis=1)
        }
        return features

    def _compute_hnr(self, signal):
        autocorr = np.correlate(signal, signal, mode='full')
        autocorr = autocorr[len(autocorr)//2:]
        peak = np.max(autocorr[1:])
        noise = np.mean(autocorr[10:50])
        return 10 * np.log10(peak / (noise + 1e-8))

Voice Prosthetic Control

Articulatory-to-Acoustic Mapping

Text-to-Speech for Laryngectomy

class VoiceProsthetic:
    def __init__(self):
        self.tts_model = self._load_tts()
        self.articulatory_decoder = self._load_decoder()

    def synthesize_speech(self, emg_signal):
        articulatory_params = self.articulatory_decoder.predict(emg_signal)
        return self.tts_model.synthesize(articulatory_params)

Dysarthria Assessment

intelligibility Score