Music Search encounters a significant challenge as users increasingly rely on catchy lines from lyrics to search for both new releases and other popular songs. Integrating lyrics into existing lexical search index or using lyrics vector index pose difficulties due to lyrics text length. While lexical scoring mechanisms like BM25 are inadequate and necessitates complex query planning and index schema for long text, text embedding similarity based techniques often retrieve noisy near-similar meaning lyrics, resulting in low precision. This paper introduces a proactive approach to extract catchy phrases from song lyrics, overcoming the limitations of conventional graph-based phrase extractors and deep learning models, which are primarily designed for extractive summarization or task-specific key phrase extraction from domain-specific corpora. Additionally, we employ a multi-step mechanism to mine search query logs for potential unresolved user queries containing catchy phrases from lyrics. This involves creation of word and character k-gram index for lyric chunks, careful query and lyrics domain-centric normalization (and expansion) and a re-ranking layer incorporating lexical and well as semantic similarity. Together these strategies helped us create a high retrieval source specifically for serving lyrics intent queries with high recall.

Music and media search spell correction is distinct as it involves named entities like artist, album and podcast names, keywords from track titles and catchy phrases from lyrics. Users often mix artist names and keywords from track title or lyrics making spell correction highly contextual. Data drift in search queries caused during calendar event days or a newly released music album, brings a unique challenge of quickly adapting to new data points. Scalability of the solution is an essential requirement as the Music catalog is extremely large. In this work, we build a multi-stage framework for spell correction solution for music, media and named entity heavy search engines. We offer contextual spelling suggestions using a generative text transformer model and a mechanism to rapidly adapt to data drift as well as different market needs by using parameter efficient based fine tuning techniques. Furthermore, using a reinforcement learning approach our spell correction system can learn from a user's implicit and explicit feedback in real-time. Some key components of this system are being used in search at Amazon Music and showing significant improvements in customer engagement rate and other relevant metrics.