Simple Speech Recognition with Hidden Markov Model (HMM)

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 Implement an HMM to recognize spoken words or phrases from audio data.Speech recognition with HMM is fascinating. Here’s a simplified roadmap to get us started:

  1. Dataset Preparation: Gather a dataset of audio recordings for the words or phrases you want to recognize. You can use open-source datasets like the Common Voice dataset by Mozilla.

  2. Feature Extraction: Extract useful features from the audio recordings, typically Mel-Frequency Cepstral Coefficients (MFCCs), which represent the short-term power spectrum of sound.

  3. HMM Model Definition: Define the states and transitions for your Hidden Markov Model. Each state can represent a phoneme or part of a word.

  4. Training the HMM: Use the extracted features to train your HMM. Libraries like hmmlearn in Python can be helpful for this step.

  5. Recognition: Implement the Viterbi algorithm to find the most likely sequence of states (words or phrases) given a new audio recording.

  6. Evaluation: Test your model with new audio recordings to evaluate its accuracy and fine-tune parameters if necessary.

Here’s a very basic example using Python and the hmmlearn library to recognize counting from 1 to 10:

First, you need to install the necessary libraries:

python
pip install hmmlearn librosa pydub webrtcvad numpy

HMM Speech Recognition Code:


import numpy as np
import librosa
from hmmlearn import hmm

# Function to extract MFCC features from audio file
def extract_features(file_name):
    y, sr = librosa.load(file_name)
    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
    return np.mean(mfccs.T, axis=0)

# Training data (placeholders for actual file paths and labels)
file_paths = ["1.wav", "2.wav", "3.wav", "4.wav", "5.wav", "6.wav", "7.wav", "8.wav", "9.wav", "10.wav"]
labels = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Extract features for each audio file
X = np.array([extract_features(file) for file in file_paths])
lengths = [1] * len(labels)

# Define and train the Hidden Markov Model
model = hmm.GaussianHMM(n_components=10, covariance_type='diag', n_iter=1000)
model.fit(X, lengths)

# Function to predict the label of a new audio file
def predict(file_name):
    features = extract_features(file_name).reshape(1, -1)
    logprob, seq = model.decode(features)
    return seq[0] + 1

# Example usage
print(predict("test.wav"))  # Replace 'test.wav' with the path of the test audio file

Start write the next paragraph here


if you get this error 

> from pydub import AudioSegment

E:\python-projects\hand_gesture_dynamic\handgesture_venv\Lib\site-packages\pydub\utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work

  warn("Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning)


It means you have not installed the `ffmpeg` into your system. To do that all you have to do is to open a PowerShell in administrative mode and then use choco install ffmpeg . this will install the fmpeg package into your system. 






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