Downstream Speech

Downstream speech processing focuses on leveraging pre-trained self-supervised speech models (like Wav2vec 2.0 and Whisper) to improve performance on various tasks, ranging from phoneme recognition and speaker identification to wake-word detection and multi-talker speech transcription. Current research emphasizes optimizing the interface between these pre-trained models and task-specific prediction heads, exploring different feature combination strategies and investigating the models' effectiveness across diverse speech types (including children's speech and noisy environments). These advancements are significant because they enable more efficient and robust speech processing applications, particularly in resource-constrained settings and challenging acoustic conditions.