What was the baseline?
a model trained on LibriSpeech data with SAT and a with a LM also trained with LibriSpeech
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.
The exploding multimedia content over the Internet, has created a new world of spoken content processing, for example the retrieval BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , browsing BIBREF5 , summarization BIBREF0 , BIBREF5 , BIBREF6 , BIBREF7 , and comprehension BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 of spoken content. On the other hand, we may realize there still exists a huge part of multimedia content not yet taken care of, i.e., the singing content or those with audio including songs. Songs are human voice carrying plenty of semantic information just as speech. It will be highly desired if the huge quantities of singing content can be similarly retrieved, browsed, summarized or comprehended by machine based on the lyrics just as speech. For example, it is highly desired if song retrieval can be achieved based on the lyrics in addition. Singing voice can be considered as a special type of speech with highly flexible and artistically designed prosody: the rhythm as artistically designed duration, pause and energy patterns, the melody as artistically designed pitch contours with much wider range, the lyrics as artistically authored sentences to be uttered by the singer. So transcribing lyrics from song audio is an extended version of automatic speech recognition (ASR) taking into account these differences. On the other hand, singing voice and speech differ widely in both acoustic and linguistic characteristics. Singing signals are often accompanied with some extra music and harmony, which are noisy for recognition. The highly flexible pitch contours with much wider range BIBREF12 , BIBREF13 , the significantly changing phone durations in songs, including the prolonged vowels BIBREF14 , BIBREF15 over smoothly varying pitch contours, create much more problems not existing in speech. The falsetto in singing voice may be an extra type of human voice not present in normal speech. Regarding linguistic characteristics BIBREF16 , BIBREF17 , word repetition and meaningless words (e.g.oh) frequently appear in the artistically authored lyrics in singing voice. Applying ASR technologies to singing voice has been studied for long. However, not too much work has been reported, probably because the recognition accuracy remained to be relatively low compared to the experiences for speech. But such low accuracy is actually natural considering the various difficulties caused by the significant differences between singing voice and speech. An extra major problem is probably the lack of singing voice database, which pushed the researchers to collect their own closed datasets BIBREF12 , BIBREF15 , BIBREF17 , which made it difficult to compare results from different works. Having the language model learned from a data set of lyrics is definitely helpful BIBREF15 , BIBREF17 . Hosoya et al. BIBREF16 achieved this with finite state automaton. Sasou et al. BIBREF12 actually prepared a language model for each song. In order to cope with the acoustic characteristics of singing voice, Sasou et al. BIBREF12 , BIBREF14 proposed AR-HMM to take care of the high-pitched sounds and prolonged vowels, while recently Kawai et al. BIBREF15 handled the prolonged vowels by extending the vowel parts in the lexicon, both achieving good improvement. Adaptation from models trained with speech was attractive, and various approaches were compared by Mesaros el al. BIBREF18 . In this paper, we wish our work can be compatible to more available singing content, therefore in the initial effort we collected about five hours of music-removed version of English songs directly from commercial singing content on YouTube. The descriptive term "music-removed" implies the background music have been removed somehow. Because many very impressive works were based on Japanese songs BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , the comparison is difficult. We analyzed various approaches with HMM, deep learning with data augmentation, and acoustic adaptation on fragment, song, singer, and genre levels, primarily based on fMLLR BIBREF19 . We also trained the language model with a corpus of lyrics, and modify the pronunciation lexicon and increase the transition probability of HMM for prolonged vowels. Initial results are reported.