# Addressing Text-Dependent Speaker Verification Using Singing Speech
## 1 Introduction  
Automatic speaker verification (ASV) is the verification of a speaker's identify based on his/her speech signals [1]. It is an important biometric technology and can be widely used in access control security authentication, personalized services, etc. Performances of ASV systems have been significantly improved by recent advances in speech technology. The state-of-the-art ASV systems are robust to session and channel variations [2; 3; 4; 5]. However, they are vulnerable to spoofing attacks, such as spoofed speech produced using either text-to-speech or voice conversion technologies [6; 7]. It is still very challenging to use the current technologies for real ASV applications. To improve the robustness of ASV systems under real applications, most previous works focused on exploring new speaker modeling algorithms, discriminative biometric patterns, or finding countermeasures to eliminate the acoustic mismatches between training and testing or spoofing speech, such as the self-attentive speaker embeddings in [8] for a better speaker identity representation, the end-to-end speaker modeling framework in [9], the light convolution neural network [10] and attentive filtering network modeling architectures in [11] for detecting spoofing utterances, etc. [12; 13]. In the literature, most of these previous works extracted the speaker information from natural speech, either the read speech or spontaneous or contextual speech [1]. We only found very few works focus on exploiting robust speaker information from other prospectives, such as speaker recognition in [14; 15; 16], where the humming was investigated to extract the speaker identity information. Specifically, in [14; 15], various acoustical features such as the linear prediction cepstral coefficients, the conventional Mel-frequency cepstral coefficients (MFCCs) and perceptually linear prediction coefficients were evaluated for humming speech. In [16], the variable length Teager energy based mel frequency cepstral coefficients was proposed to identify speakers from their hum. Moreover, the speaker-dependent characteristics were extracted from the nasals for forensic speaker recognition [17]. In [18], the humming, singing and speech were compared and evaluated as biometric signal. They found that the humming sounds are better for capturing speaker-specific characteristics than speech and singing. In [19], GMM mean supervector was used to improve performance of speaker clustering with speech from both reading and singing. The authors of [20; 21] proposed the timbre and vibrato-motivated acoustic features for singer identification. All of these previous works provide a good reference for us to study speaker recognition from a new perspective. However, we have not found any previous works that examine and compare the speaker verification performances between using the natural Mandarin reading speech and singing speech. For example, the authors of [14; 15; 16; 18] only designed text-independent speaker verification tasks; their speech corpus and songs were all of Hindi languages and only a second-order polynomial classifier was used in their speaker classification. In [14; 16], only humming speech was examined for human verification and identification, no comparison results was presented, such as the performance comparison between the humming and the natural reading speech. In this paper, we also focus on the speaker verification using the singing speech. However, the big difference between this work and the above-mentioned previous works is that we focus on examining and comparing the effectiveness of using normal Mandarin reading speech and their corresponding singing speech for short-time text-dependent speaker verification. Different features and three types of speaker modeling approaches were also investigated, including the conventional and state-of-the-art deep neural network based techniques.