Transcribing Lyrics From Commercial Song Audio: The First Step Towards Singing Content Processing
This addresses the challenge of processing singing content for applications like retrieval and browsing, but it is an initial attempt with incremental improvements.
The paper tackled the problem of transcribing lyrics from commercial song audio, which is difficult due to varying phone durations and pitch contours, and achieved a word error rate (WER) of 73.90%, significantly lower than the baseline of 96.21%.
Spoken content processing (such as retrieval and browsing) is maturing, but the singing content is still almost completely left out. Songs are human voice carrying plenty of semantic information just as speech, and may be considered as a special type of speech with highly flexible prosody. The various problems in song audio, for example the significantly changing phone duration over highly flexible pitch contours, make the recognition of lyrics from song audio much more difficult. This paper reports an initial attempt towards this goal. We collected music-removed version of English songs directly from commercial singing content. The best results were obtained by TDNN-LSTM with data augmentation with 3-fold speed perturbation plus some special approaches. The WER achieved (73.90%) was significantly lower than the baseline (96.21%), but still relatively high.