VCTK Dataset

Step 1: Download the VCTK dataset and extract the audio and transcript files.

Step 2: Preprocess the data, aligning audio with transcripts and extracting acoustic features.

Step 3: Train a speaker-adaptive TTS model using the preprocessed data.

Step 4: Evaluate the model's performance on unseen speakers from the dataset.

Step 1: Select a target speaker from the VCTK dataset.

Step 2: Extract the audio data for the chosen speaker.

Step 3: Train a voice cloning model using the speaker's audio.

Step 4: Test the model by generating speech from new text prompts.

Step 1: Categorize speakers in the VCTK dataset by their accents.

Step 2: Train an accent recognition model using the audio data and accent labels.

Step 3: Evaluate the model's accuracy in identifying accents in new speech samples.

Step 4: Analyze the model's errors to improve its performance.

Step 1: Use the VCTK dataset to train a neural vocoder.

Step 2: Integrate the vocoder with a text-to-speech system.

Step 3: Evaluate the naturalness of the synthesized speech through subjective listening tests.

Step 4: Fine-tune the system based on the evaluation results.

Step 1: Annotate portions of the VCTK dataset with emotional labels.

Step 2: Train an expressive TTS model using the annotated data.

Step 3: Control the generated speech's emotional tone.

Step 4: Evaluate the effectiveness of the emotional expressiveness.