This paper introduces three cancelable speaker identification techniques based on the spectrogram estimation of speech signals subjected to either chaotic encryption process, or RSA algorithm in addition to Radon transform to produce cancelable templates instead of the original speech signals. The resulting transformed versions of the voice biometrics are stored in the server instead of the original biometrics. Therefore, the users' privacy can be protected well. It is evident from the obtained results that the proposed techniques are secure, reliable and practical. They have good encryption and ability to generate cancelable templates. These characteristics lead to good performance. The proposed cancelable speaker identification techniques are evaluated under the influence of Additive White Gaussian Noise (AWGN) with different strengths. This makes them more accurate in identifying the users and also more resistant to attack attempts. In addition, security is enhanced through maintaining the confidentiality of the processed data. In the experimental results, evaluation metrics such as Equal Error Rate (EER), False Rejection Rate (FRR), and False Acceptance Rate (FAR) are used to assess the performance of the proposed techniques. In addition, the genuine, impostor distributions, Receiver Operating Characteristic (ROC) curve and area under the ROC curve for the proposed techniques are estimated for better evaluation and comparison.