# Addressing Text-Dependent Speaker Verification Using Singing Speech
## 2 Corpus  
Since there is no publicly released Mandarin corpus that meets our motivation, we designed a new corpus for our research. It includes both the normal reading and singing speech, thus we named this corpus as \"RSS\". A detailed description of RSS is shown in Table 1. It consists of 20 speakers, including 10 male and 10 female undergraduate students. This study was our preliminary work on exploring and comparing the Mandarin singing speech and normal reading speech for speaker verification. Our motivation was to examine the effectiveness of different speaking styles for speaker verification. Therefore, to eliminate the interference from complicate recording setups, we only selected the undergraduate students with age ranges from 22 to 24 as our target speakers. We selected 10 lyrics segments that are familiar to everyone as the text for audio recording. Around 5-15 utterances were included in each lyrics segments. Three music styles of these lyrics were selected: pop music, classical music and country music. These music genres are popular to most of the students in our university. During speech recording, the speaker used a common laptop built-in microphone to sing and read the given lyrics in a quiet lab environment. We used \"Audition\" as our speech recording and editing software. To create reasonable comparative experiments between normal and singing speech, for each speaker and each lyrics text, we recorded it twice for reading speech and twice for singing speech in two days. Each lyrics segment covered around 10-30 s speech. Each recording was formatted as 16 kHz, 16 bit WAV file. We chose the specific wav file format to record the audio, because this format is more generally used for human-machine interaction applications. Moreover, as 16 kHz WAV format is also normally used in automatic speech recognition applications, it would be better to keep the same speech input setup to make things compatible in real-world applications. All of the recordings are in Mandarin. Table 2 gives the details of the ASV tasks we designed based on the \"RSS\" corpus. In most application scenarios, the enrollment and test data per speaker were normally around 1-3 s. Therefore, we should cut our long recordings into 1 s and 3 s short segments. Taking the 1 s-1 s-GD task with reading speech as example, for each of the 20 target speakers, he/she had 10 distinct lyrics that cover 10-30 s reading speech, and each lyric was recorded twice. To construct our ASV tasks, we first cut all these long speech segments into many small segments according to the completeness of the sentence text (lyric). For all of the 10-30 s audio files with the same lyric, we segmented them according to the same lyric integrity. Then, we picked out all of the short segments with length around 1 s. Finally, we chose the hold-out cross-validation method to design our experiments. Because each speaker had two recordings with the same lyrics, we randomly selected one of the two short segments with the same lyric text as the 1 s training (enrollment) segment of target speaker and the other as test. There was no overlap between the speech of speaker training and test. This test segment was used to test all of the other target speaker models. Therefore, from all of these selected 1 s segments, we obtained 600 segments as the target speaker enrollment speech, 300 for males and 300 for females. There were some enrollment segments belong to the same speaker, but they were treated as separate speakers to have their own speaker models in our experiments. This is because, to make the observations in our experiments more general, we should try to increase the number of the target enrollment speakers as much as possible. To construct the gender-dependent test trials, such as for one speaker of 10 females with one lyric, we then had 30 enrollment segments (1 s per each) to train 30 separate target speaker models, and 30 segments for test.