Speech Recognition:

• End-to-End Speech Recognition: Building an end-to-end speech recognition system with RNNs, LSTMs, or GRUs, where the input is a sequence of audio features (MFCC) and the output is the predicted transcription.
• Automatic Speech Recognition with Connectionist Temporal Classification (CTC): Using CTC loss to handle the alignment between input and output sequences when they are of different lengths.

pythonCopy codeimport tensorflow as tf
from tensorflow.keras import layers

# Define a basic RNN model for speech recognition
def build_model(input_dim, output_dim):
    model = tf.keras.Sequential([
        layers.Input(shape=(None, input_dim)),
        layers.LSTM(128, return_sequences=True),
        layers.LSTM(128, return_sequences=True),
        layers.Dense(output_dim, activation='softmax')
    ])
    return model

model = build_model(input_dim=13, output_dim=29)  # Example with 13 MFCC features
model.compile(optimizer='adam', loss='ctc_loss')